Elsevier

Journal of Health Economics

Volume 79, September 2021, 102485
Journal of Health Economics

The dynamics of the smoking wage penalty

https://doi.org/10.1016/j.jhealeco.2021.102485Get rights and content

Abstract

Cigarette smokers earn significantly less than nonsmokers, but the magnitude of the smoking wage gap and the pathways by which it originates are unclear. Proposed mechanisms often focus on spot differences in employee productivity or employer preferences, neglecting the dynamic nature of human capital development and addiction. In this paper, we formulate a dynamic model of young workers as they transition from schooling to the labor market, a period in which the lifetime trajectory of wages is being developed. We estimate the model with data from the National Longitudinal Survey of Youth, 1997 Cohort, and we simulate the model under counterfactual scenarios that isolate the contemporaneous effects of smoking from dynamic differences in human capital accumulation and occupational selection. Results from our preferred model, which accounts for unobserved heterogeneity in the joint determination of smoking, human capital, labor supply, and wages, suggest that continued heavy smoking in young adulthood results in a wage penalty at age 30 of 15.9% and 15.2% for women and men, respectively. These differences are much smaller than the raw difference in means in wages at age 30. We show that the contemporaneous effect of heavy smoking net of any life-cycle effects explains 62.9% of the female smoking wage gap but only 20.4% of the male smoking wage gap.

Introduction

Smoking cigarettes is associated with well-documented expected longevity (Darden, Gilleskie, Strumpf, 2018, Doll, Peto, Boreham, Gray, Sutherland, 2004) and health care expenditure (Sloan, Ostermann, Conover, Taylor, Picone, 2006, Xu, Bishop, Kennedy, Simpson, Pechacek, 2015) effects. In addition, smokers earn between 2% and 24% lower wages than nonsmokers (Auld, 2005, Grafova, Stafford, 2009, van Ours, 2004), but neither the magnitude nor the mechanisms behind the smoking wage gap are well understood. A simple set of explanations is that smokers are less productive due to higher rates of absenteeism and illness, and they generate higher health care costs, which may be particularly important to firms that self-insure.1 Similarly, employers may have preferences (increasingly over time) for nonsmokers, and the smoking wage gap may emerge because labor market opportunities are less forthcoming to smokers (Hotchkiss, Pitts, July 2013, Roulin, Bhatnagar, 2018). While the literature has focused on these types of static mechanisms, differences in wages are inherently a dynamic process: smoking behavior and human capital formation begin at young ages, and these endogenous states influence decisions regarding education, labor supply, smoking, and occupation. Failing to account for the joint determination of these histories will lead to biased estimates of the smoking wage gap. Furthermore, there is little empirical evidence on the importance of these life-cycle mechanisms relative to static explanations, especially in early-career workers for whom human capital is still developing.

We estimate a dynamic model of human capital accumulation, wage determination, and smoking behavior to decompose static and life-cycle mechanisms behind the smoking wage gap. In our model, state variables that capture past smoking behavior, work decisions, and human capital accumulation influence the joint decisions to work, smoke, and seek education. Conditional on working, we observe wages and occupational task requirements, which we define as factors that characterize occupations on the basis of mental reasoning, social skills, and physical strenuousness.2 While task requirements shed light on productivity differences as a mechanism for the smoking wage gap, explicitly modeling the sequence of school enrollment decisions characterizes the extent to which those who choose to smoke accumulate lower human capital. An important feature of our dynamic empirical model is the treatment of unobserved heterogeneity, which we allow to be flexibly correlated across behavioral and outcome equations. If, for example, unobserved factors cause some individuals to be more likely to select into physically demanding jobs and to be more likely to start (and continue) smoking, we allow for this correlation. Finally, given that there are approximately 55 million former smokers in the United States, our model allows us to investigate the extent to which wages of former smokers converge to those of nonsmokers (Creamer et al., 2018).

We estimate our model with geocoded data from the National Longitudinal Survey of Youth, 1997 Cohort, which allows us to (a) evaluate competing mechanisms for the wage gap among a modern cohort of workers and (b) track the initial wage trajectories for smokers and nonsmokers, beginning with schooling and proceeding through a person’s early career. In our raw data, the smoking wage gap between nonsmokers and near-daily smokers emerges by age 23 and grows to roughly 40% for women and 25% for men by age 30. To demonstrate the importance of accounting for observed and unobserved heterogeneity, we simulate our estimated model in which we impose different patterns of smoking behavior from age 16 through age 30 while updating state variables that capture past smoking, work, and education decisions. These simulations suggest that the selectivity-corrected age 30 smoking wage gap is 15.9% and 15.2% for women and men, respectively, although the mechanisms differ substantially by gender. The dramatic reduction between our simulated smoking results and those from the raw data is due to the strong positive correlation in factors that drive abstinence from smoking and human capital accumulation. These findings are broadly consistent with the literature (Auld, 2005, Grafova, Stafford, 2009, van Ours, 2004), but they stem from a model-based approach which is conducive to isolating mechanisms.

Our simulated results reflect the total marginal effect of smoking on wages – the combination of static and life-cycle mechanisms. To separate the static mechanisms of smoking on wages, we simulate our model, updating schooling, employment, and occupation decisions/outcomes as though an individual does not smoke while imposing different smoking behaviors in the determination of wages. Thus, wages progress as a function of nonsmoker employment, education, and occupation paths, but, in some simulations, as though individuals were smoking when wages are determined. We call this the contemporaneous effect of smoking on wages, and our simulation results imply that this contemporaneous effect explains 62.9% the total female smoking wage gap but only 20.4% of the male gap. That is, the majority of the smoking wage gap for men stems from factors that are pre-determined when wages are set; for women, on the other hand, the contemporaneous effect explains the majority of the smoking wage gap. This finding - that mechanisms behind the smoking wage gap are very different for men versus women - extends the literature that the smoking wage gap differs by gender (van Ours, 2004).

Part of the difference in contemporaneous versus life-cycle effects by gender is due to occupation. For women at age 30, the simulated smoking wage gap is increasing in the amount of mental reasoning required in an occupation - the smoking wage penalty is approximately 36.6% larger for women in the most mentally taxing occupations relative to the least - but invariant to its physical strenuousness. Similarly, the simulated wage penalty is 24.4% larger for socially demanding jobs. For men, the smoking wage gap is nearly 60% larger in the most mentally taxing jobs and 36% larger for the most socially demanding jobs; however, the gap becomes smaller in the degree of physical strenuousness. The smoking wage gap is roughly 14.8% smaller for men in the most physically demanding occupations relative to the least. Our findings with respect to physical strenuousness also suggest that productivity and health differences, which should be exacerbated as an occupation becomes more physically demanding, are unlikely to drive the smoking wage gap for either men or women. For men, the fact that the smoking wage gap is smaller for more physically strenuous jobs creates an incentive for male smokers to select into more physically strenuous occupations. As educational attainment translates to more mentally rigorous, more socially demanding, and less physically strenuous occupations, the opportunity cost of an additional year of schooling for male smokers is higher than for female smokers.

Both the magnitude and the mechanisms behind the smoking wage gap have significant policy relevance. Not only does smoking have implications for individual lifetime earnings, but the external implications for lower income tax revenues and productivity could be significant. Food and Drug Administration (FDA) cost-benefit and cost-effectiveness analyses already take these consequences into account (e.g., MacMonegle et al., 2018), and our results may inform future regulatory action, including regulatory action with respect to substitute nicotine delivery products such as e-cigarettes (Cotti et al., 2020). Accounting for selection into smoking and individual heterogeneity, our analysis provides a more accurate estimate of the direct impact of smoking on wages, and we find that while the smoking wage gap is larger for women than for men, the returns to quitting are also more immediate and significant for women.

Section snippets

Background

The debate over regulating tobacco use in the U.S. dates back at least to 1964 when then U.S. Surgeon General at the time, Dr. Luther Terry, released the first government report concluding that cigarette smoking causes lung cancer, in addition to other types of cancer. The report also concludes, while there is no one characteristic that is uniquely identifiable with smokers, “The overwhelming evidence points to the conclusion that smoking-it’s beginning, habituation, and occasional

Evidence from NLSY

The National Longitudinal Survey of Youth, 1997 Cohort (NLSY97) offers a unique look at human capital accumulation and early-career wage progression among a nationally representative sample of the United States. The data contain rich, longitudinal information on wages, occupation, employment, education, and smoking for up to 18 years, beginning when respondents were between the ages of 12 and 18. Our data include geocoded information that, in some cases, is down to the Metropolitan Statistical

Dynamic model

Recognizing the limitations of the statistics above, we turn to the dynamic model of labor supply in Keane and Wolpin (1997). Those authors estimate a model, starting at age 16, of NLSY79 Cohort men who make annual decisions regarding schooling and labor supply. Individuals in the model may seek additional schooling, work in either formal employment or military service, or engage in home production, each of which has dynamic implications for future wage offers. We propose a dynamic system of

Parameter estimates

Table 6 presents parameter estimates from the wage equation. Columns labeled as “separately” present coefficients and standard errors from estimation of the wage equation separately from other equations in the model, and columns labeled “jointly” present estimates from our preferred model in which all equations are estimated together with correlated errors. Hence, columns labeled jointly present estimates of μ2wμ4w. In our preferred system model, the parameter estimates themselves are

Discussion and conclusion

This research employs a dynamic model of human capital accumulation, wage determination, occupational outcomes, and smoking behavior that allows us to decompose the contemporaneous and life-cycle effects of the smoking wage gap. The model is dynamic in the sense that current period outcomes of wages and occupational task requirements are a function of both current period and past work, smoking, and education decisions. We allow errors that dictate these behavioral and outcome equations to be

CRediT authorship contribution statement

Michael E. Darden: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. Julie L. Hotchkiss: Conceptualization, Data curation, Investigation, Project administration, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. M. Melinda Pitts: Conceptualization, Data curation, Investigation, Project

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    We thank Tom Mroz, Melinda Morrill, John Cawley, Michael Pesko, and seminar participants at the Federal Reserve Bank of Atlanta and the Southeastern Micro Labor Workshop for helpful comments. We also thank Patrick Henson for excellent research assistance. The views expressed in this work are those of the authors and do not reflect the opinion of the Federal Reserve Bank of Atlanta or the Federal Reserve System.

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