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Intertemporal discounting as a risk factor for high BMI: Evidence from Australia, 2008

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  • Dodd, Mark C.

Abstract

This paper explores the relationship between intertemporal discounting and body weight, using stated preference measures of intertemporal discounting, and the body mass index (BMI) to represent relative body weight. The empirical analysis uses Australian data obtained in 2008 through the South Australian Health Omnibus Survey. A quantile regression analysis is used to allow the marginal effects of the explanatory variables on BMI to vary across the conditional BMI distribution. It is shown that an indicator of intertemporal discounting elicited from a hypothetical monetary trade-off has a significant positive relationship with BMI. This relationship appears to be stronger in the upper quantiles, but there is insufficient statistical evidence for this difference. Evidence is presented that intertemporal discounting is a risk factor for increased BMI with a magnitude of effect comparable to more commonly recognized risk factors such as income and education. However there is no significant relationship found between BMI and an alternative indicator of intertemporal discounting elicited from trade-offs in health status.

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  • 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.
  • Handle: RePEc:eee:ehbiol:v:12:y:2014:i:c:p:83-97
    DOI: 10.1016/j.ehb.2013.05.005
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    Cited by:

    1. Buchmueller, Thomas C. & Johar, Meliyanni, 2015. "Obesity and health expenditures: Evidence from Australia," Economics & Human Biology, Elsevier, vol. 17(C), pages 42-58.
    2. Koffi-Ahoto Kpelitse & Rose Anne Devlin & Sisira Sarma, 2014. "The Effect of Income on Obesity among Canadian Adults," Working Papers 14C002, Canadian Centre for Health Economics.
    3. Kang, Myong-Il & Ikeda, Shinsuke, 2016. "Time discounting, present biases, and health-related behaviors: Evidence from Japan," Economics & Human Biology, Elsevier, vol. 21(C), pages 122-136.
    4. Victor Champonnois & Olivier Chanel, 2016. "How useful are (Censored) Quantile Regressions for Contingent Valuation?," Working Papers 2016.12, FAERE - French Association of Environmental and Resource Economists.

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