• 検索結果がありません。

Second, they are positively associated with nicotine dependence

N/A
N/A
Protected

Academic year: 2023

シェア "Second, they are positively associated with nicotine dependence"

Copied!
32
0
0

読み込み中.... (全文を見る)

全文

This article examines smoking, including nicotine addiction, the most common form of addiction, on the basis of the quasi-hyperbolic discounting approach proposed by Laibson (1997). Interestingly, they discussed that the most important factor would be differences in beliefs about the health consequences of smoking between residents of the United States. Next, I discuss two points regarding the measurement of the economic-psychological parameters adopted in this paper.

The analysis shows that a 1% increase in the direct effect parameter decreases the proportion of smokers with low nicotine dependence by 0.5444%, but increases the proportion of smokers with high nicotine dependence by 0.4259%. Furthermore, a 1% increase in the intolerance parameter reduces the proportion of smokers with low levels of nicotine dependence by 0.9324%. Based on the FTND, current smokers were classified as heavy smokers (H), moderate smokers (M), or light smokers (L).

In this section, I explain the derivation of the impatience and immediacy parameters and show the estimation results. Based on the exponentially discounted utility model, when the utility of 100,000 JPY in the present is equal to the utility of 150,000 JPY in X years, I obtain the. On the other hand, if I include too few, the description of the alternatives becomes inadequate.

8 The adoption of the Halton sequence diagram is an important issue to be investigated (Halton 1960).

ESTIMATION MODEL AND RESULTS

Estimation model

Another anomaly is observed in that the measured values ​​of 1-RISK are negative, indicating that the samples are somewhat prone to risk. However, all the relative risk aversion coefficients are not statistically significant, so the respondents are completely risk neutral. Perhaps because the functional forms I assumed are so specific that unobserved interdependencies between parameters are not sufficiently addressed.9.

Although many studies have investigated the relationship between smoking and attitudes towards risk, the issue remains inconclusive (Mitchell 1999, Reynolds et al. 2003, Ohmura et al. 2005)10. I reserve final judgment on the results for the moment because my aim is to investigate whether the impatience and immediacy parameters are associated with smoking, including nicotine dependence. are classified into three groups depending on FTND scores) with a binomial probit model (in which smoking is indicated by 1 and non-smoking by 0). In the binomial model, the dummy variable is 1 for smoking and 0 for non-smoking; in the ordered probit model, the variable for nicotine dependence ranges from 0 (low) to 2 (high).

First, the individual characteristic variables are a female dummy variable (GENDER = 0 for male, 1 for female), age (AGE), age squared (AGESQ), school history (SCHOOL = 1 for high school, 2 for school high school, 3 for university and 4 for graduate school), and annual family income (INCOME, million JPN).

Estimation Results

DISCUSSIONS

The second hypothesis is formulated for the elasticity of the probability of smoking with respect to the immediacy effect. Van der Pol (2011) examined the role of time preference in the relationship between education and health, and concluded that the effect of education decreases but does not disappear after controlling for time preference. If one assumes that smoking is the result of utility anomalies, higher consistency naturally leads to a lower probability of smoking.

For example, Gruber and Koszegi (2001) demonstrated that some smokers failed to understand the real difficulty of quitting smoking. Kan (2007) empirically studied time-inconsistent preferences in the context of smoking behavior and concluded that some smokers who wanted to quit had a demand for control devices, eg, smoking bans in public areas. or increasing cigarette taxes. Further, the elasticities of nicotine dependence with respect to the impatience and immediacy parameters are displayed in Table 5.

The third hypothesis is established for the elasticities of nicotine dependence with respect to the time preference rate. A 1% increase in the time preference rate decreased the proportion of low nicotine dependent smokers by 0.9324% with 5% significance, but increased the proportion of high nicotine dependent smokers by 0.9522% with 1% significance. However, a 1% increase in the time preference rate did not affect the proportion of moderately nicotine dependent smokers.

In view of this result, the time preference rate accounts for the higher and lower degree of nicotine dependence, which is consistent with the findings of previous research. For example, Reynolds et al. 2004) reported a significant positive correlation between the number of cigarettes smoked daily and the time preference rate, and Ohmura et al. 2005) suggested that both the frequency of nicotine self-administration as well as the dose were positively associated with greater delay discounting. The fourth hypothesis is established for the elasticities of nicotine dependence with respect to the immediate effect.

A 1% increase in the immediate effect decreased the proportion of smokers slightly dependent on nicotine by 0.5444% at 10% significance, but increased the proportion of smokers highly dependent on nicotine by 0.4259% at 5% significance. However, a 1% increase in immediate effect did not affect the proportion of smokers moderately dependent on nicotine. The immediate effect also successfully accounts for nicotine addiction; this finding is consistent with previous research.

CONCLUSIONS

Therefore, the ML selection probability is the weighted average of the logit probabilities Lnit(βn), evaluated at the parameter βn by the density function f(βn), which can be written as. In the form of a linear parameter, the utility function can be written as Unit =βn'xnit +εnit,. Since the ML choice probability is not expressed in closed form, simulations must be performed to estimate the ML model (see Train 2003, p. 148 for details).

One can also calculate the estimator of the conditional mean of the random parameters, conditional on individual specific choice profile yn, given as. Delay discounting in current and never-before cigarette smokers: similarities and differences across item, sign, and size. Discounting of delayed health gains and losses in current, never, and former cigarette smokers.

Delay and probability discounting in relation to varying degrees of adolescent smoking and non-smoking. Note 2: IMPATIENCE and 1-RISK are estimated for both time-inconsistent and time-inconsistent samples, while ln IMMEDIACY is estimated only in time-inconsistent samples.

TABLE 1: Basic Demographics
TABLE 1: Basic Demographics

SMOKER

SMOKER

SMOKER

TABLE 1: Basic Demographics
FIGURE 1: Research Strategy
FIGURE 2: Representative Questionnaire
TABLE 2: Impatience, Immediacy, and Risk Parameters
+3

参照

関連したドキュメント

Seminar/Workshop Presentations: Hokkaido University, February 18, 2011 - “A Model of Stochastic Utility Smoothing” Hitotsubashi University, December 2, 2010 - “A Model of Stochastic