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Smoking Behavior of Older Adults: A Panel Data Analysis Using HRS

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Abstract

Using longitudinal data from Health and Retirement Surveys over 1992–2010, this paper analyzes decisions by older American to continue smoking and the number of cigarettes to consume using two-part hurdle models with correlated effects. We build on the existing literature by incorporating a myriad of factors including cigarette prices, health shocks and smoke-free laws in one econometric framework. Our estimates indicate that higher cigarette prices play an important role in both reducing participation and the intensity of consumption even for this adult population. In addition, health shocks, as measured by newly diagnosed diseases, raise the probability of quitting, highlighting the ‘curative’ aspects of cessation. However, we find very little effect of health on smoking intensity if an older adult does not quit after a health shock. Per capita cigarette consumption in the US declined by over 64% during the period. We show that increased cigarette prices and health shocks together contribute almost equally to explain nearly 86% of the decline, with little that can be attributed to smoking bans and anti-smoking sentiment.

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Fig. 1

Source: The Tax Burden on Tobacco (2012), 2012

Fig. 2
Fig. 3

Source: The Tax Burden on Tobacco (2012) and authors’ calculation using HRS

Fig. 4
Fig. 5

Source: Authors’ calculation using HRS survey in 1994

Fig. 6

Source: The Tax Burden on Tobacco (2012) and authors’ calculation

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Notes

  1. Centers for Disease Control and Prevention. Smoking and tobacco use. http://www.cdc.gov/tobacco/data_statistics/fact_sheets/fast_facts. Accessed August 18, 2016.

  2. Centers for Disease Control and Prevention. Economic facts about US tobacco production and use. http://www.cdc.gov/tobacco/data_statistics/fact_sheets/economics/econ_facts. Accessed August 18, 2016.

  3. The biennial Indian HRS known as LASI (Longitudinal Ageing Study in India) is currently in its 4th round, which started in 2010. In China, HRS sister survey CHARLS was implemented for a national representative sample of persons 45 years of age or older since 2011. These surveys are harmonized with HRS in the U.S. Thus, the methodology adopted in this paper can be implemented in India and China using LASI and CHARLS, respectively.

  4. Many studies have demonstrated the bias in fixed effects estimation of non-linear models with short panels, see Fernandez-Val (2009), Hahn and Kuersteiner (2011) and others.

  5. See Chamberlain (1984), Wooldridge (1995), Mundlak (1978) and Labeaga (1999).

  6. Although previous studies have typically used linear regressions (cf. Maclean et al. 2016), our comparison shows that the count model outperforms linear regression in our application in terms of model fit. Detailed results are available upon request. See also Wang and Heitjan (2008).

  7. The Tobacco Use Supplement to the Current Population Survey (TUS-CPS) is a national survey of tobacco use as part of the US Census Bureau’s Current Population Survey in 1992–1993, 1995–1996, 1998–1999, 2000, 2001–2002, 2003, 2006–2007, and 2010–2011.

  8. Pesko et al. (2016) use intra-state variations in cigarette prices due to local taxes to show, though for a much younger sample, that the resultant price elasticity is almost tripled compared to state-level prices. Since most of these local taxes are overwhelmingly concentrated in Alabama, Missouri and Virginia, we included a dummy variable representing these three states and its interaction with the state price variable in our cessation and conditional consumption equations. These additional controls were statistically insignificant in out estimations.

  9. We should point out that since HRS does include few individuals in their 1930s and 1940s (except due to the inclusion of younger spouses), the peaks of age profiles at 36 or 50 should be taken with due caution.

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Acknowledgements

This research was supported by the National Institute on Minority Health and Health Disparities, National Institutes of Health (Grant number 1 P20 MD003373). The content is solely the responsibility of the authors and does not represent the official views of the National Institute on Minority Health and Health Disparities or the National Institutes of Health. We thank an anonymous referee for making a number of useful comments that has helped to revise our paper.

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  1. Department of Economics, University at Albany, SUNY, Albany, NY, 12222, USA

    Kajal Lahiri & Xian Li

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  1. Kajal Lahiri
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Correspondence to Kajal Lahiri.

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Lahiri, K., Li, X. Smoking Behavior of Older Adults: A Panel Data Analysis Using HRS. J. Quant. Econ. 18, 495–523 (2020). https://doi.org/10.1007/s40953-020-00196-x

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