5.2 Regression Results
5.2.2 Individual Support Regressions
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From Table 9, the dependent variables of interest were addiction levels, daily consumption, and number of quit attempts in the past 12 months. Here, many of the results were significant, and exhibiting the same negative effect on support for the dependent variables. The largest beta was the 10% reduction in support for restaurant bans for those with high levels of addiction. This is consistent with the Ikeda finding [33]
that naïf hyperbolic discounters show higher rates of addiction and consumption because this group doesn’t foresee their future self control problem, and therefor doesn’t demand costly self-imposed external controls. The exception is the positive demand, albeit relatively small, for cigarette taxes as the number of failed quit attempts in the last 12 month increases. The literature review suggested a causal mechanism for this as self-awareness increases, thus suggesting people are transient between naïve and sophisticated in their forecasting.
5.2.2 Individual Support Regressions
The second and third sets of regressions are about this paper’s most important
contribution: testing for utilization of individual cessation supports like counseling, quit lines, and medication. Because of data attrition, the independent variables that we were most interested in, addiction levels, daily consumption, and number of failed quit
attempts in the past 12 months, were included in different regressions. Consequently, the reporting tables look different than the previous results.
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Table 10. Individual Support Regression 1-3 (from left to right)
* p<0.10, ** p<0.05, *** p<0.010
* p<0.10, ** p<0.05, *** p<0.010
* p<0.10, ** p<0.05, *** p<0.010
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Table 11. Individual Support Regression 4-6 (from left to right)
* p<0.10, ** p<0.05, *** p<0.010
* p<0.10, ** p<0.05, *** p<0.010
* p<0.10, ** p<0.05, *** p<0.010
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From table 10, the only significant predictor of cessation support (quit lines or counseling) utilization was a high addiction level. Those who reported smoking a
cigarette within five minutes of waking up demanded higher levels of counseling or quit-lines. Because these types of support serve to make quitting less awful, the difference is noteworthy.
From table 11, the results are slightly ambiguous. High addiction levels and consumption increase demand for these supports, however a high number of failed quit attempts decreases demand for medication. It is definitely worth investigating the causal mechanism behind this observation, and opens the door to future research.
Comparing table 10 and 11, an increase in income reduces demand for time intensive supports like quit lines and counseling, but increases demand for the less time intensive medication. Logically, this is because of increased opportunity cost of lost time. Health care coverage had significance across the board and increases demand for all types of cessation supports: counseling, quit lines, and medication. This has important policy implications.
6 Discussion
The first set of hypotheses concerned the demand for costly external cessation supports as predicted by observed smoking behavior, which was argued to be a causal pathway for manifestations of consequences per theoretical model.
For the high addiction level predictor, there are somewhat inconsistent results based on the type of ban. Only the restaurant ban has explanatory power from this variable. The high addiction level decreases demand for smoking bans at restaurants. This is consistent with the Ikeda correlation. [27]
The model with cigarette taxes was the only regression that yielded any significant results for quit attempts. Hersch [24] had shown that previous quit attempts increased the
demand for smoking bans using panel data. We were not able to replicate his results for smoking bans, however the result for taxes is similar. We can speculate that the possible
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reason could be that as smokers increase in quit attempts, their self-awareness changes them from naïf to sophisticated.
For consumption levels, our model yielded the most consistent results. For each of the external cessation support categories, higher levels of cigarette consumption decreased the demand. This is consistent with the Ikeda [27] analysis linking the naïve hyperbolic type to higher rates of consumption. However, it runs contrary to the Hersch findings.
Both this study and the Hersch study did not have information about time preferences and discounting, so we were not able to disaggregate the types. The Ikeda study, on the other hand, didn’t answer questions about support for external cessation supports. Because the models remain indeterminate in these questions, future research would be invaluable in trying to explain the links between types, behaviors, and preferences throughout a smoking lifecycle.
The second set of hypotheses were about the previously un-researched questions about smoking behaviors and support for individual cessation supports.
The first type of support was assistance like quit lines, counseling, or therapy. Of the predictor variables only level of addiction had a significant p-value. Here, there was a modest increase in demand for these types of cessation support. Although fuzzy, being a male did decreases the demand in one sample. In all the regressions, demand was also decreased with higher income, which could be explained by the higher opportunity cost of these therapies. Across the groups, however, having healthcare coverage that paid for these supports increased demand. Here, there is a salient policy implication: the
government could subsidize these types of treatments to increase utilization levels.
For nicotine replacement therapy or medicine support categories, higher levels of
addiction increased the demand. This is consistent under all of the models, given that the expected reward in utility is greater than price. An important policy implication is that, regardless of likely heterogeneity amongst smoking groups, this type of support remains attractive.
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Higher daily consumption levels also, albeit modestly, increased the demand for nicotine replacement therapy or medicine. Logically, this makes sense because higher levels of consumption will likely translate to higher disutility in the withdrawal phase. A similar rationale is behind the addiction proxy. Therefore, regardless of type, reducing disutility from smoking cessation is demanded.
A high number of quit attempts decreased the demand for nicotine replacement therapy or medication. This is an important finding because it is a different type of predictor than addiction levels and consumption, in that it is actualized failure to carry out the desired task. The mental process that differs between addiction levels, daily consumption, and failed quit attempts could be further investigated.
Lastly, amongst these types of support, healthcare coverage increased demand as did a higher income. Here, income moves demand in the opposite direction compared to the time-intensive quit lines or counseling.
Comparing the regressions between the external supports like taxes and bans compared to individual supports like counseling or medication, this also introduces an inconsistency because it implies awareness and forecasting about disutility in the withdrawal period, but not about time inconsistencies. Further research would benefit by asking questions to obtain information about time preferences, discounting, and sophistication or naivety.
7 Conclusion
Lastly, models of smoking are largely used for prescriptive reasons: policy aimed at reducing smoking. Therefore this paper takes a special interest in explaining the
heterogeneity of preferences amongst the group that is currently smoking, but expresses a desire to quit. While it may be more difficult for policymakers to collect information about time preferences and discounting, a link between certain observed behaviors from our survey – quit attempts, addiction levels, and consumption – may be more readily observable.
Finally, also recognizing that different supports will be utilized differently, this paper was the first to explore how quit attempts, addiction levels, and consumption influence the
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utilization of individual cessation supports. High addiction levels and high consumption levels increased demand for nicotine replacement therapy and medication.
Future research is still required to sort out the heterogeneity in observed behavior as it relates to time preferences and support for different types of cessation supports. Like this, public policy can have different approaches available given different observed
characteristics of smoking groups.
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