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The effect of health shocks on smoking and obesity

Abstract

Aim

To investigate whether negative changes in their own health (i.e. health shocks) or in that of a smoking or obese household member, lead smokers to quit smoking and obese individuals to lose weight.

Methods

The study is informed by economic models (‘rational addiction’ and ‘demand for health’ models) which offer hypotheses on the relationship between health shocks and health-related behaviour. Each hypothesis was tested applying a discrete-time hazard model with random effects using up to ten waves of the German Socioeconomic Panel (GSOEP) and statistics on cigarette, food and beverage prices provided by the Federal Statistical Office.

Results

Health shocks had a significant positive impact on the probability that smokers quit during the same year in which they experienced the health shock. Health shocks of a smoking household member between year t−2 and t−1 also motivated smoking cessation, although statistical evidence for this was weaker. Health shocks experienced by obese individuals or their household members had, on the other hand, no significant effect on weight loss, as measured by changes in Body Mass Index (BMI).

Conclusion

The results of the study suggest that smokers are aware of the risks associated with tobacco consumption, know about effective strategies to quit smoking, and are willing to quit for health-related reasons. In contrast, there was no evidence for changes in health-related behaviour among obese individuals after a health shock.

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Notes

  1. Measures for smoking prevalence differed depending on the data source: Telephone surveys conducted by the RKI reported that rates for men decreased from 39% in the early nineties to 38% in 2003, while rates for women increased from 27.5 to 30.1% (where n = 7,341 in 2003) [2]. Data from GSOEP indicates that smoking prevalence declined for young people aged 12 to 17 years, from roughly 28% in 2001 to about 15% in 2008 (n = 25,000 per year [but not all answered the health questionnaire]) [44]. Data on cigarette consumption per head strongly suggest a decrease in consumption from about 2,000 cigarettes in 1970 to 1,000 in 2008 [2]. In this study, I chose to refer to data of the Mikrozensus provided by the Federal Statistical Office because it has the largest and, therefore, most representative sample size (n = 824,380 individuals; 1% of the population were asked health questions in 2005) [3].

  2. The model is based on Stigler and Becker’s model on addiction [16].

  3. In the first year, the variable takes a value of zero. If the individual does not alter his lifestyle in the second year, it takes a value of one. If there is no lifestyle change in the third year, it takes a value of two and so on. It is reset to zero if the individual “dies” (i.e. quits smoking or loses weight and then starts counting up again).

  4. The standard method for modelling time dependence in binary data has been to incorporate time dummies or splined time in logistic regressions. Carter and Signorino showed however that the cubic polynomial of the temporal variable (t, t 2, t 3) outperformed time dummies in Monte Carlo analysis and performed as well as splines [24].

  5. Other studies have used measures of waist-hip or waist-height ratio, or waist circumference. These measures are not available in the GSOEP.

  6. The study also showed that rising cigarette prices lead to an increase in body weight in the US [45]. As, however, the latter finding was contested by Gruber et al., who found the opposite effect using cigarette taxes rather than prices as a variable in their explanatory model [46], I did not factor cigarette taxes or prices into the equation.

  7. The GSOEP does not indicate if the individual is pregnant in the respective time period or not. However, pregnancy is an important predictor for quitting smoking and invalidates BMI measures. Therefore, a proxy for pregnancy is constructed. The proxy assumes that a woman is pregnant if a child between zero and one year of age becomes a new household member in the consecutive time period.

  8. For example, I make no attempt to resolve the debate over whether increasing food and beverages prices play a major role in explaining obesity.

  9. The analysis was conducted on a sample with ten waves and six durations per individual for the smoking equation and on a sample with ten waves and only four durations for the obesity equation.

  10. Cawley was careful to note that the impact of rising prices may be two-fold, such that rising prices may decrease overall consumption or incentivise individuals to opt for cheaper and higher caloric food [34].

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Correspondence to Leonie Sundmacher.

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Sundmacher, L. The effect of health shocks on smoking and obesity. Eur J Health Econ 13, 451–460 (2012). https://doi.org/10.1007/s10198-011-0316-0

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Keywords

  • Economics of Obesity
  • Economics of Smoking
  • Habit Formation
  • Discrete-Time Duration Model

JEL classification

  • C41
  • I18
  • I19