University of Colorado Boulder
Income and Smoking: The Effects of Family Income on Smoker Status
Remy Heinala
Alexander Zyles
Van Pelt Collamer
Noah Feldman
Joe Felton
Professor. Klein
Applied Econometrics ECON 4848
4 May 2023
Introduction
In the United States, there are many gaps between low-income households and highincome ones. One such gap is tobacco use and its prevalence. Our research project is based on the theory that low income individuals show higher smoking prevalence. In a research paper by Mills et al.(2020), the researchers found that between 2011 and 2017, the disparity between low and high income groups in terms of smoking decreased in only a single state. In three states, that disparity actually widened. In the rest of the states, the differences stayed the same. In fact, they found that the prevalence of smoking in low-income groups was nearly twice as much as in higher-income groups. The researchers also found that in the majority of states, odds of smoking decreased after controlling for age, sex, race, and education level. The study was meant to document trends in Adult smoking habits based on income, showing that there has been little progress made in narrowing that gap. This comes at a time when being conscious of one's own health is of great importance.
Smoking can cause many negative effects on an individual’s health. In another study from researcher Ciapponi (2011) from the World Health Organization (WHO), they found that deaths related to tobacco use are expected to grow to more than 8 million per year. This is an increase from the current 5 million deaths per year due to tobacco use. While the majority of these deaths are borne by low and middle-income countries, the disparities can be seen in highincome countries as well in the context of health issues that smoking presents. The text states that in a recent United States study with a sample of 300,000 men, mortality declined progressively across 12 categories of household - including “cigarette smoking” - as income
increased from less than $7500 to more than $32 499. (WHO) By understanding what factors contribute to the reason for lower income individuals and households bearing the bulk of deaths from tobacco use would allow for governments to be able to target policy to reduce it.
Some policies have been raising the price of cigarettes. In research by Franks et al. (2007), they found that as the real price of cigarettes increased over time, the income disparity of smokers widened. Given that this consumer behavior violates economic demand theory, they offered the explanation that individuals most addicted to cigarettes are the least likely to change behavior in response to the price change. The research shows that there is more that needs to be done to overcome this disparity than just simply raising the price. Those that are most addicted to and reliant on tobacco will still spend their limited income on cheap stress relievers even if the price of smoking rises.
When it comes to cigarette use, the study by A.C. Villanti et al. (2017) shows modest correlation between household income and cigarette use; however, using subjective financial status as a measure - how well an individual thinks they are doing financially -, a stronger inverse correlation is found. Individuals who are not meeting their basic needs show much higher daily cigarette use than those who live comfortably. From this, we have concluded that individuals who feel socioeconomically disadvantaged tend to show higher prevalence in smoking.
We hypothesize that the effect of household income on the probability that someone is a smoker is actually negative. This means that as income levels increase, the likelihood of someone being a smoker decreases. This follows along with what prior research has suggested. It is also in line with Mills et al. (2020) findings that smoking decreased when one of the variables
that was controlled for was education. This makes sense because a household is likely to have a higher income if composed of individuals with higher levels of education.
In line with previous literature on the subject, our paper found that as family income levels increased, the probability of an individual being a smoker decreased. Additionally, we ran separate regressions for those that have a college degree and do not. Doing so we found that there is a greater income effect on the probability of a person being a smoker when they don't have a college degree compared to those with a college degree. However, for both groups, income still had a significant effect on the probability of a person being a smoker.
Data
We gathered data from the Current Population Survey May 2019 database to explore the relationship between family household income and individual smoker status. For our dependent variable, we are choosing smoking status. The independent variables we chose for our regression are metropolitan status, family income of a household, age, sex, race, marital status, employment status, and educational attainment. We believe that these variables are most likely to affect the probability of an individual being a smoker and therefore, are including them in our regression to control for them
Our dependent variable tests the probability of an individual being a smoker. To do this, we split the smoker recode variable into two dummy variables: one for if an individual is a current smoker - either daily or non-daily – (smoker) and one for if the individual is a former smoker or has never smoked (notSmoker). Our main independent variable for our research is family income levels which we split into 6 categorical dummy variables – all of which are measured in United States dollars: one for family income strictly less than 30,000 dollars; one for family income between 30,000 and 49,999 dollars; one for family income in between 50,000 and
74,999 dollars; one for family income between 75,000 and 99,999 dollars; one for family income in between 100,000 and 149,999 dollars; and lastly one for family income equal to or above 150,000 dollars. We decided to make 6 bins of family income measured like this as each bin contains roughly around 16.7% or 1/6th of the observations for family income. Furthermore, we created dummy variables for metropolitan status to distinguish an individual living in or around a metropolitan area and one not living in a metropolitan area. A dummy variable was also created for gender to distinguish the effects on males and females. To understand the effects between race, dummy variables were created for White, Black, Asian, Native American, and other race identifying respondents. Additionally, we split our education variable into 4 categorical dummy variables: no high school diploma, high school diploma, some college education, and college education or more. We also created two dummy variables for employment status as working and not working. Lastly, we chose to separate married, divorced, and unmarried individuals with their own dummy variables as well. Age is the only continuous variable in the regression analysis. Although, we did decide to restrict our analysis to only individuals aged 18 to 85 years old, as children and adolescents are not a subject of interest in our study
We believe these variables provide us with key demographic information that can influence the probability of an individual being a smoker.
Table 1 depicts a summary of the variables in the regression. In May of 2019, 89.3% of the sample in our regression reported not smoking, with the average age of a respondent being about 50 ½ years old. The largest percentage of educational attainment for this sample is 36.6% of respondents reporting they have at least a four-year college degree or more. On average, the majority of respondents were also working, with 61.2% of the sample being employed. While we did try to split family income evenly, it is worth noting that the largest part of the sample’s income distribution (approximately 22%) falls under our “Low Income” dummy variable. Lastly, 81.2% identify as white.
can see that the average age for both groups is close to fifty.
helps to show that there may not be a large gap in ages between both our groups.
Figure 2 illustrates the percentage of smokers amongst different income groups. For our research, we are most interested in seeing how family household income impacts the likelihood of a person being a smoker. This bar chart shows smoking prevalence progressively falling as you increase familial income levels. Approximately 17% of all individuals in our lowest family income group, making less than $30,000 per year in household income, were smokers. This percentage then drops to about 11% in our lower middle-income group and 8% in our higher middle-income group (household income from $50,000 to $74,999 and $75,000 to $99,999 respectively). In comparison, our sample shows that less than 5% of all individuals in our highest income group, making $150,000 or more in household income, answered to being a smoker.
Results
A Binomial Probit regression model is used to measure the change in probability of an individual being a smoker (dependent variable) by their family income level (independent variable). The other variables mentioned in the data section were included to avoid omitted variable bias. The regression equation being estimated is:
The regression will exclude nonsmoker, low income, not in metro, no high school diploma, female, white and never married. These will act as the comparison group for their respective dummy variables.
Table 2 reports the marginal effects of each control variable on the probability of an individual being a smoker. Since this is a Binary Probit model, the reported marginal effects of being a smoker are in percentage points. In this interpretation, we can see that having a family income of 150,000 dollars or more decreases the likelihood of an individual being a smoker by 5.8 percentage points compared to the group with a family income of less than 30,000 dollars, holding all other independent variables constant. Generally, we see that in the model, as family income increases, the percentage point probability of being a smoker decreases when compared to the low family income group holding all other variables constant.
Another factor that was important in determining the probability of an individual being a smoker was their education. The results show that compared to individuals who do not have a
high school diploma, those with some college education or more are less likely to be smokers. The exception to this is those who have a high school diploma which increases the probability of being a smoker by 0.5 percentage points from those who do not have one
To further analyze the effects of family income on an individual's smoker status, an additional regression was estimated that was conditional on the individual's educational attainment. This meant we estimated a new equation that excluded the dummy variables for education and then estimated regressions for if an individual had a college and didn't have a college degree. The new equation being estimated for the regression is:
The excluded variables in this equation are: nonsmoker, low income, not in metro, no high school diploma, high school diploma, some college, college or more, female, white and never married. These will act as the comparison group for their respective dummy variables, with the exception of the education dummy variables. These were excluded so that the effects of income on smoker status can vary depending on whether an individual has a college degree or not. The results of the regression are summarized below in Table 3.
Table 3 reports the marginal effects of each of the independent variables on the probability of an individual being a smoker. This is still a Binomial Probit model and as such reports the marginal effects in terms of percentage point probability. Unlike Table 2 which included the effects from the education dummy variables, this regression instead reports differing effects for individuals who have a college degree and those who don’t. The results show that there is a greater effect of family income on the probability of someone being a smoker when they don't have a college degree compared to those who do have a college degree. For both groups, the probability of being a smoker decreased with higher levels of income, but the probability decreased at a greater rate for those without a college degree. Individuals who have a family income between $75,000 and $99,999 and no college degree were 6.9 percentage points less likely to be a smoker compared to those with a family income less than $30,000 and with no college degree. However, individuals in the same income bracket with a college degree were only 1.6 percentage points less likely to be a smoker compared to those with a family income less than $30,000 and having a college degree. This is the same story for each of the income brackets. This means that income has a greater effect on the probability of an individual being a smoker when they do not have a college degree compared to individuals with a college degree. The results also show that males without a college degree are 4.8 percentage points more likely to be a smoker than females without a college degree while males with a college degree are only 1 percentage points more likely to be a smoker than females with a college degree. This is in line with the results from Table 2 that showed having a college degree makes an individual 8.3 percentage points less likely to be a smoker compared to those without a high school diploma.
Additionally, running a separate regression analysis for those with and without a college degree generally resulted in those with a college degree having a lower percentage point
probability of being a smoker. However, this wasn't the case for respondents living in a metropolitan area. Those without a college degree living in a metropolitan area were 3.4 percentage points less likely to be a smoker than those not living in a metropolitan area. However, when looking at respondents with a college degree, those living in a metropolitan area were only 0.8 percentage points less likely to be a smoker than those not in a metropolitan area. This means that all else equal, living in a metropolitan area without a college degree has a larger negative effect on the likelihood of being a smoker than those with a college degree. Both of these variables were also statistically significant at the five percent level meaning that there was a difference for both groups in the effects of living in a metropolitan area compared to living in a rural area. A second interesting point was that the effect of being in the other race category and having a college degree was statistically significant meaning there was a significant effect on the likelihood of being a smoker for those respondents compared to white respondents. However, this was not the case for respondents in the other race category that did not have a college degree representing no real difference between them and white respondents.
The results also show that the magnitudes of the coefficients did change when running a regression for those with and without a college degree. However, the signs of these coefficients stayed the same. The Pseudo R2 value also went down for the regressions compared to when including the education dummy variables.
Conclusion
Our research found a negative correlation between family income and smoking status amongst the individuals in our sample. It confirms our hypothesis that people within higher income groups have a lower probability of smoking compared to individuals within lower income groups; that is, as income rises, the likelihood of being a smoker falls. We ran two
additional regressions for those with a college degree and without which showed that education also plays a factor. Overall, the income effects of being a smoker on individuals with college degrees was smaller as income levels rose. Individuals without a college degree however, showed a greater income effect on the probability of being a smoker. The difference in absolute value between the high-income groups and low income groups was greater for individuals without a college degree than those with a college degree. This negative correlation may be due to lower education attainment levels within our lower income group. For all individuals who hold a college degree, we see income effects are much lower on smoking probability. People with a college degree may have been better educated on the negative effects of smoking on their health. However, it is also assumed that less educated on average have lower paying jobs. This negative relationship may also be due to financial stress factors. The Mental Health Foundation states that smoking can be a form of self-medication to stress. “Nicotine creates an immediate sense of relaxation, so people smoke in the belief it reduces stress and anxiety” (Mental Health Foundation). People with lower household incomes probably have much more financial constraints than those with higher levels of income and smoking can be a cheap way to find stress relief for these individuals. Nicotine is an addictive substance and if these stressors continue to persist, it may be more likely for low-income individuals (who are probably more prone to these stressors) to continue with this relatively inexpensive poor self-care habit.
An extension to the research conducted in this paper would be to use panel data rather than cross-sectional data. Having access to panel data for this research would show how smoking status changed for individuals over time as their income levels changed. For this study, we were only able to look at current smokers. This meant for the purpose of the study that respondents
who answered “former smoker” were considered as nonsmokers. However, panel data could possibly allow us to see the change in an individual's smoker status over time and how it moves with their fluctuations in income. If someone moved up the income ladder, we may see this person quit smoking. It may also allow us to look at individuals who picked up smoking over time and follow where they fall in the income bracket pre and post smoker status. Additionally, we could see if individuals who quit smoking but started again later reflect fluctuations in their income. This could be due to economic conditions like the 2008 housing market crash that saw an increase in the unemployment rate. We would then be able to make inferences about the relationship between smokers quitting rates and upward social mobility.
Additionally, panel data using individuals from a younger age group may allow us to see smoking prevalence in adults from those who started before the age of 18. This could be interesting to see if individuals from lower income childhood backgrounds show higher levels of smoking than those from higher income childhood backgrounds. We would then be able to identify with a wider age range when smoking adults started smoking and who they were in terms of socioeconomic status.
Works Cited
Ciapponi, Agustín, and World Health Organization. "Systematic review of the link between tobacco and poverty." (2014). https://apps.who.int/iris/handle/10665/44453
Franks, Peter, et al. "Cigarette prices, smoking, and the poor: implications of recent trends."
American Journal of Public Health 97.10 (2007): 1873-1877
https://ajph.aphapublications.org/doi/epub/10.2105/AJPH.2006.090134
Mills, Sarah D., et al. "Are state-level income-based disparities in adult smoking declining?."
Preventive medicine 133 (2020): 106019 https://www-sciencedirectcom.colorado.idm.oclc.org/science/article/pii/S0091743520300438?via%3Dihub
Sarah Flood, Miriam King, Renae Rodgers, Steven Ruggles, J. Robert Warren and Michael Westberry. Integrated Public Use Microdata Series, Current Population Survey: Version 10.0 [dataset]. Minneapolis, MN: IPUMS, 2022.
https://doi.org/10.18128/D030.V10.0
“Smoking and Mental Health: Smoking and Stress” Mental Health Foundation.
https://www.mentalhealth.org.uk/explore-mental-health/a-z-topics/smoking-and-mentalhealth#:~:text=Nicotine%20creates%20an%20immediate%20sense,someone%20may%2 0feel%20that%20way Accessed 4 May 2023
Villanti, Andrea C., Amanda L. Johnson, and Jessica M. Rath. "Beyond education and income: identifying novel socioeconomic correlates of cigarette use in US young adults."
Preventive medicine 104 (2017): 63-70
https://discovery.ebsco.com/c/3czfwv/details/rbfsgk7ld5?limiters=FT1%3AY&q=%28% 28Tobacco%20AND%20use%29%29%20AND%20%28Income%29%20AND%20%28
%28United%20AND%20States%29%29
