* 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
3.1 Variable Settings
In the following section, we will present our variables of interest and corresponding questions used in their construction.
The NATS has several questions about smoking policies. Particularly, we are interested in demand for different types of smoking cessation methods. Cigarette taxes and smoking bans are externally imposed, costly controls - they would increase the cost thereby
reducing the net benefit of future cigarette consumption. On the contrary, counseling or nicotine replacement therapy are aimed at reducing the disutility associated with smoking cessation during the withdrawal period.
This study is interested in these different supports because the theoretical models specify which type of support a particular type of individual would likely use. The rational, time consistent addicts would never use any costly external control that reduces the net benefits in the future stream of utility from cigarette consumption. Naïve hyperbolic discounters are also not aware they even need this type of external control, so they would not opt for anything costly. Then, the only group opting for this type of costly, external control is the sophisticated hyperbolic discounters – they know they may not be time
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consistent with future choices, but are willing to pay with future utility in order to coerce compliance with the present plans. Lastly, disutility minimizing supports like nicotine replacement therapy, quit lines, or counseling could be utilized by any type of individual.
3.1.1 Dependent Variables
1. Cigarette Tax (bincigtax) captures support for cigarette taxes. It is a binary variable created from the question, “Would you be in favor of an increase in the tax on a pack of cigarettes if the money were used to improve the public’s health?”
2. Restaurant Smoking Bans (binrestban) is a binary variable created from the question,
“Should smoking indoors in restaurants [always/sometimes/never] be allowed?” Never was assigned 1 as it is a position against smoking.
3. Bar Smoking Bans (binbarban) is a binary variable created from the question, “Should smoking indoors in bars, casinos, or clubs [always/sometimes/never] be allowed?”
4. Park Smoking Bans (binparkban) is a binary variable created from the question,
“Should smoking at parks [always/sometimes/never] be allowed?”
5. Cessation Assistance (binfutasst) regards whether people who want to quit smoking will seek cessations supports. It is created from the question, “When you try to quit smoking, do you plan to use a telephone quit line, a class or program or one-on-one counseling from a health professional to help you quit?”
6. Medication Usage (binfutmed) measures whether people plan to use medication to quit smoking. It is created from the question, “When you try to quit smoking, do you plan to use a nicotine patch, nicotine gum, lozenges, nasal spray, an inhaler, or pills such as Wellbutrin, Zyban, buproprion, Chantix, or varenicline to help you quit?”
All of the dependent variables are binary. For variables 1-4, a preference in favor of a tax or ban is coded as 1. For variables 5 and 6, a preference for seeking out cessation
assistance or medication is coded as 1.
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3.1.2 Independent Variables
The main research focus of this paper is to examine how heterogeneity in the smoking population with a desire to quit affects demand for different types of smoking cessation support. For example, naïve hyperbolic discounters smoke more cigarettes, have more failed quit attempts, and have a higher likelihood of initiating smoking. [33] Those with a present bias also have a higher level of addiction. [26]
Under predictions of the empirical models, only the sophisticated hyperbolic discounters have a preference for costly “penalties” for future smoking: taxes, bans. Kan has found a link between a desire to quit smoking and a support for costly pre-commitment, however was not able to disaggregate the population according to sophistication or naivety, or smoking behaviors. [16] The NATS does not have questions about time preferences, discounts, or sophistication or naivety. However, it does have questions about behaviors that have a demonstrated association with the differently modeled individuals:
1. Addiction levels (binaddicthigh) was constructed from the question, “How soon after you wake up do you usually have your first cigarette?” This was coded as a binary variable signifying high addiction levels if a cigarette was smoked within five minutes of waking up.
2. Daily consumption (SMOKPERDAY) is an ordinal variable constructed from the question, “On average, about how many cigarettes a day do you now smoke?”
3. Previous quit attempts (QT12MOS) is an ordinal variable constructed from the
question, “During the past 12 months, how many times have you tried to quit smoking for good?”
4. Gender (bingender) is a control variable which is specified as 1 if gender is male.
5. Age (AGE) is a control variable that is ordinal and takes the range of 18-113.
6. Education (Educa2_r) is an ordinal control variable. The original answers were recoded 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
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4=Post High School Certificate or Diploma, or Associate degree 5=Bachelor's Degree 6=Master's, Professional, or Doctoral degree.
7. Marriage (binmarriage) is a binary control variable. Those responding as married were coded as 1.
8. Household income (INCOME2) was coded as an ordinal variable. 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.
9. Health insurance coverage (hccpayqs) that pays for individual cessation supports like counseling or medication is included in the regressions about healthcare utilization. If an individual’s insurance payed for counseling, quitlines, or medication being used as an aid in cessation, then it was coded as 1.