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Unravelling the influence of smoking initiation and cessation on premature mortality using a common latent factor model

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  • Silvia Balia
  • Andrew M. Jones

Abstract

Duration models for lifespan and smoking, that focus on the socio-economic gradient in smoking durations and length of life, are estimated controlling for individual-specific unobservable heterogeneity by means of a latent factor model. The latent factor influences the risk of starting and quitting smoking as well as the hazard of mortality. Frailty could in°uence smoking behaviour through two mechanisms: the effect of life expectancy on initiation of smok- ing and the impact of adverse health events on quitting. Our findings suggest that individual-specific preference for experimentation, which leads those peo- ple who start smoking soonest to quit early, is a potential source of spurious correlation between smoking durations. They also suggest that frailty acts according to both mechanisms, driving selection into early smoking initiation as well as selection into early smoking cessation. Overall, determinants of smoking durations and mortality hazard are largely unaffected by unobserv- able heterogeneity. However, the latent factor model strengthens the results of the univariate models suggesting that increasing the quitting rate and reduc- ing the duration of smoking would decrease premature mortality. Whereas, prompting people to delay starting would shorten the length of time spent smoking.

Suggested Citation

  • Silvia Balia & Andrew M. Jones, 2007. "Unravelling the influence of smoking initiation and cessation on premature mortality using a common latent factor model," Health, Econometrics and Data Group (HEDG) Working Papers 07/06, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:07/06
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    1. Mroz, Thomas A., 1999. "Discrete factor approximations in simultaneous equation models: Estimating the impact of a dummy endogenous variable on a continuous outcome," Journal of Econometrics, Elsevier, vol. 92(2), pages 233-274, October.
    2. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
    3. Jerome Adda & Valérie Lechene, 2004. "On the identification of the effect of smoking on mortality," CeMMAP working papers CWP13/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
    5. Balia, Silvia & Jones, Andrew M., 2008. "Mortality, lifestyle and socio-economic status," Journal of Health Economics, Elsevier, vol. 27(1), pages 1-26, January.
    6. van Ours, Jan C., 2002. "A pint a day raises a man's pay; but smoking blows that gain away," IZA Discussion Papers 473, Institute for the Study of Labor (IZA).
    7. Martin Forster & Andrew M. Jones, 2001. "The role of tobacco taxes in starting and quitting smoking: Duration analysis of British data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(3), pages 517-547.
    8. Lee Lillard & Constantijn Panis, 1996. "Marital status and mortality: The role of health," Demography, Springer;Population Association of America (PAA), vol. 33(3), pages 313-327, August.
    9. Partha Deb & Pravin K. Trivedi, 2006. "Specification and simulated likelihood estimation of a non-normal treatment-outcome model with selection: Application to health care utilization," Econometrics Journal, Royal Economic Society, vol. 9(2), pages 307-331, July.
    10. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    11. Valerie Lechene & Jéróme Adda, 2001. "Smoking and Endogenous Mortality: Does Heterogeneity in Life Expectancy Explain Differences in Smoking Behavior?," Economics Series Working Papers 77, University of Oxford, Department of Economics.
    12. Farrell, Phillip & Fuchs, Victor R. & Fuchs, Victor R., 1982. "Schooling and health : The cigarette connection," Journal of Health Economics, Elsevier, vol. 1(3), pages 217-230, December.
    13. van Ours, Jan C., 2004. "A pint a day raises a man's pay; but smoking blows that gain away," Journal of Health Economics, Elsevier, vol. 23(5), pages 863-886, September.
    14. Michelle M. Mello & Sally C. Stearns & Edward C. Norton, 2002. "Do Medicare HMOs still reduce health services use after controlling for selection bias?," Health Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 323-340.
    15. Dana P. Goldman, 1995. "Managed Care as a Public Cost-Containment Mechanism," RAND Journal of Economics, The RAND Corporation, vol. 26(2), pages 277-295, Summer.
    16. Kajal Lahiri & Jae G. Song, 2000. "The effect of smoking on health using a sequential self-selection model," Health Economics, John Wiley & Sons, Ltd., vol. 9(6), pages 491-511.
    17. Gabriel A. Picone & Frank A. Sloan & Shin-Yi Chou & Donald H. Taylor, 2003. "Does Higher Hospital Cost Imply Higher Quality of Care?," The Review of Economics and Statistics, MIT Press, vol. 85(1), pages 51-62, February.
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    Cited by:

    1. Erdogan-Ciftci, Esen & van Doorslaer, Eddy & Bago d'Uva, Teresa & van Lenthe, Frank, 2010. "Do self-perceived health changes predict longevity?," Social Science & Medicine, Elsevier, vol. 71(11), pages 1981-1988, December.

    More about this item

    Keywords

    smoking; mortality; duration analysis; unobservable heterogeneity; latent factors;

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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