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Related Experiment Videos

The effect of smoking on health using a sequential self-selection model.

K Lahiri1, J G Song

  • 1Department of Economics, State University of New York at Albany, 12222, USA. KL758@cnsvax.albany.edu

Health Economics
|September 13, 2000
PubMed
Summary

Individual risk beliefs significantly influence smoking decisions. Self-selection in quitting, but not starting, aligns with economic rationality, suggesting true smoking risks differ from sample averages.

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Area of Science:

  • Health Economics
  • Behavioral Economics
  • Econometrics

Background:

  • Individual smoking behavior is complex, influenced by perceived risks and economic factors.
  • Understanding self-selection in smoking initiation and cessation is crucial for accurate health risk assessment.
  • Previous models may not fully capture the interplay between risk beliefs and smoking decisions.

Purpose of the Study:

  • To estimate a structural model of individual smoking behavior focusing on risk beliefs.
  • To analyze self-selection effects in smoking initiation and cessation decisions.
  • To determine if self-selection in these decisions aligns with economic rationality.

Main Methods:

  • Developed a five-equation structural model including selection and switching regressions.

Related Experiment Videos

  • Estimated the model using Full Information Maximum Likelihood (FIML).
  • Utilized starting values from a heteroskedasticity-corrected Heckman-Lee two-step method.

    • Main Results:

    • Significant self-selection was found in both smoking initiation and cessation.
    • Self-selection in cessation decisions, but not initiation, is consistent with economic rationality.
    • Sample-based health effect estimates may misrepresent true smoking risks due to self-selection.

    Conclusions:

    • Individual risk beliefs play a key role in smoking choices.
    • Economic rationality appears to guide cessation decisions more than initiation.
    • Accurate assessment of smoking's health impact requires accounting for self-selection biases.