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to quit, and have more failed quit attempts [25]. Ida 2014 [26] uses the Fagerström Test for Cigarette Dependence to create a latent variable of addiction levels and finds a correlation between present-biased utility and high addiction levels.
However, the Grignon and Ida papers didn’t disaggregate naïve and sophisticated individuals. Ikeda 2014 [27] disaggregates sophisticates and naïfs using panel data.
Under Cragg’s two part model, the results indicate that discounting rates affect the
probability of initiating smoking and secondly, the number of cigarettes consumed among smokers. Furthermore, naïve hyperbolic discounters are more likely to start smoking than sophisticated hyperbolic discounters. Lastly, the study noted that an individual’s time preferences were not stable throughout time, casting doubt on an assumption implicit to many models.
2.6 Summary and Motivation
In summary, the previous models describe populations, often but not exclusively in the context of smoking, who discount utility across time. Under the rational addiction model, the discounting factor is exponential, time consistent, and agents are forward-looking.
Quasi-hyperbolic discounting, however, adds a Beta that accounts for time inconsistent behavior. The Laibson framework explains how under the quasi-hyperbolic framework, strong biases for the present can create a perpetual procrastination loop.
Then, secondly, whether individual agents are aware of the type of their behavior - sophisticates or naïfs. Naturally, following the model assumptions outlined above, this further distinction only applies to quasi-hyperbolic discounters. Naïve agents have a Beta with a value between 0 and 1, but think they have a Beta equal to one. In the context of smoking, the group of naïve smokers would not choose to impose costly external pre-commitments upon themselves because they are not aware they could benefit from them.
The sophisticates are the self-aware quasi-hyperbolic discounters. They solve a backwards induction problem at t=0 to align their future decision tree with the current optimal. One way for them to align their future decisions with the current optimal is to increase the costs in future utility streams. This is why empirical studies have looked for evidence of smokers imposing external costs upon themselves in the form of smoking
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taxes or smoking ban - only the sophisticated quasi-hyperbolic discounters would exhibit a behavior like this.
However, smokers are a likely heterogeneous population – a mix of exponential
discounters, quasi-hyperbolic sophisticates and naïfs. Furthermore, ex-ante and ex-post preferences and behavior are likely not stable throughout time at the individual level.
While other studies have linked certain behaviors like higher addiction levels, and more failed quit attempts to naïf hyperbolic discounters, as far as we are aware, few studies have linked the behaviors to demands for cessation support. Only Hersch, [24] has explained increased demand for public smoking bans as a function of failed quit attempts and consumption. However, as far as we are aware, no papers have done similar studies for the individual cessation supports that are aimed at decreasing disutility – nicotine replacement and counseling.
This paper’s unique contribution, then, is investigating whether characteristics linked to particular types of smokers will translate to revealed preferences that are consistent with their type, and secondly, whether these same behaviors affect demand for supports that decrease disutility from cessation.
A more thorough understanding of the mechanics of cessation are important because smoking is costly. The US Surgeon general estimates that the economic costs to the US are in excess of USD150billion per year. This captures the productivity loss from mortality, but not morbidity. Healthcare costs attributed to smoking are estimated to be around 7% of total healthcare expenditures. [28] At individual level, smokers also internalize increased healthcare costs compared to lifetime nonsmokers. If healthcare premiums on account of smoking were incorporated into the price of cigarettes, each pack would have an additional USD35 on top of the purchase price. [29]
However, health is not a completely sunk cost after smoking initiation. Starting and quitting would still be preferable to starting and continuing to perpetuity, but research finds recent quitters increased healthcare utilization in the first year after quitting smoking. Then, in the following years, healthcare utilization converges to non-smoker levels. [30] Because quit attempts are a window of opportunity for behavioral change,
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information about how different types of smokers experience the cessation period is valuable.
3 Data
The statistical findings of this study are based on the 2009-2010 National Adult Tobacco Survey Questionnaire (NATS). The goal of this survey was to provide a framework to evaluate national and state tobacco control policies and characterize adult tobacco user behaviors while controlling for sociodemographic factors. [31]
The NATS was a national, cross-sectional study conducted from October 2009-February 2010. The survey uses a two-stage (house, then individual respondent), weighted
sampling procedure divided into two frames – landlines and cell phones – respectively divided into three strata: a listed landline stratum, a not-listed landline stratum, and a cellphone stratum. The listed stratum was oversampled relative to the not-listed stratum.
The target landline was equally distributed among the states, while the cell phone strata was proportional to population. Accordingly, three sets of weights were created: national estimates, state estimates, and landline estimates. [32]
The final sample size is 118,581 adults 18 or over. 67,272 have never smoked. 34,327 are ex-smokers. 16,542 are current smokers. Of the current smokers, 10467 want to quit, 5185 do not. 2846 individuals were currently quitting during the interview.
This paper will focus on the group of current smokers who have expressed a desire to quit. The descriptive statistics for the sociodemographic variables, sorted by different groups from the study are summarized in table 1.
Table 1. Descriptive Statistics from the NAT
# obs
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* observation numbers differ b/c I drop non-responses and “don’t know” responses
(1) Education is an ordinal variable with the following assignments: 1=Less Than High School Diploma, GED, or Equivalent 2=High School Diploma, GED, or Equivalent 3=Some College, no degree 4=Post High School Certificate or Diploma, or Associate degree 5=Bachelor's Degree 6=Master's, Professional, or Doctoral degree
(2) Income is an ordinal variable with the following assignments: 1= less than $20,000 2=$20,000 to less than $30,000 3= $30,000 to less than $40,000 4=$40,000 to less than $50,000 5=$50,000 to less than
$70,000 6=$70,000 to less than $100,000 7= $100,000 to less than $150,000 8= $150,000 or more