Discussion Papers In Economics
And Business

Does time preference affect smoking behavior?
A dynamic panel analysis
Takahiro Miura
Discussion Paper 16-16-Rev.

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANDoes time preference affect smoking behavior?
A dynamic panel analysis
Takahiro Miura
Discussion Paper 16-16-Rev.

June 2017

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANDoes time preference affect smoking behavior?
A dynamic panel analysis ∗Takahiro Miura †Abstract
I analyze the effect of time preference on smoking behavior while controlling for the effectof past smoking behavior, using a dynamic panel analysis. I use nationwide micro panel datafrom Japan and make three main findings. First, as in the previous literature, without controlling
for the effect of past smoking behavior, I find positive associations between time preference andsmoking behavior, with the association significant for male. Second, by using a dynamic panel
analysis, I find that time preference affects smoking behavior significantly for female but not
for male. Third, male smokers who have a low discount rate are more likely to want to quit or
reduce smoking behavior, whereas female smokers with a low discount rate are no more likely
to want to quit or reduce smoking behavior. This result implies that while male smokers areconcerned about the future consequences of smoking, they cannot manage to quit.
JEL Classification: D90, I12
Keywords: Time preference, Smoking, Dynamic panel analysis, State dependence∗

I thank Yoko Ibuka, Shinsuke Ikeda, Fumio Ohtake, and Masaru Sasaki as well as the two anonymous reviewers fortheir useful comments. This research utilizes the micro data from the Preference Parameters Study of Osaka Universitys
21st Century COE Program Behavioral Macrodynamics Based on Surveys and Experiments and its Global COE project
Human Behavior and Socioeconomic Dynamics. I acknowledge the program/projects contributors: Yoshiro Tsutsui,
Fumio Ohtake, and Shinsuke Ikeda. I would like to thank Editage (www.editage.jp) for English language editing.
†
Graduate School of Economics, Osaka University, 1-7, Machikaneyama, Toyonaka, Osaka, 560-0042, Japan.
u304898i@ecs.osaka-u.ac.jp OCRID:0000-0002-5926-0945

1

Introduction

Smoking behavior is an intertemporal decision because it provides immediate satisfaction for smokers but increases their future health risk. The field of economics has tried to explain smoking behavior in the context of an intertemporal decision problem (Becker and Murphy, 1988; Gruber andKöszegi, 2001). One prominent model used is the rational addiction model proposed by Becker
and Murphy (1988). According to their model, people with a high discount rate tend to smoke
because they place minimal value on the future consequences of smoking. Because people witha high discount rate place more weight on the present than on the future, the effect of increasing
tobacco prices is larger than for people with a low discount rate. Therefore, knowing what kinds ofpeople smoke is important for effective tobacco control policymaking.
Many empirical studies examine the association between smoking behavior and time preference; however, their results are mixed. For instance, a significant difference in time preference
is observed between current smokers and nonsmokers but not between former smokers and nonsmokers (Bickel et al., 1999). Further, the observed discount rate is significantly associated withsmoking behavior except for a female subsample (Harrison et al., 2010; Kang and Ikeda, 2014).
However, Khwaja et al. (2007) use three elicitation methods of time preference, with three different
questions concerning time preference appearing in each. All their results show an insignificant association between time preference and smoking status. Further, Grignon (2009) finds no significant
difference in the probability of smoking between patient people (i.e., those with a lower discountrate) and impatient people (i.e., those with a higher discount rate).
Most studies that examine the association between smoking behavior and time preference assume that individuals’ time preferences are invariant or unaffected by past smoking behavior. However, some economic theories argue that time preference is endogenous (Becker and Mulligan,
1997; Bommier, 2006; Orphanides and Zervos, 1998). According to these theories, past smoking
behavior may increase time preference directly or indirectly. Some empirical studies support thosetheories. Yi et al. (2008), for example, find a significant decrease in current time preference in samples who reduced cigarette smoking, whereas samples who did not reduce smoking behavior did
1not decrease their current time preferences significantly. Secades-Villa et al. (2014) use samples
who want to abstain from smoking. At the 12-month follow-up, the authors observe a significant
decrease in the current time preferences for samples who had continued to abstain from smoking at
the follow-up but not for those who had resumed smoking.
Another characteristic of smoking behavior is its persistence; current smokers tend to smoke inthe future. This property is called state dependence. The theory of smoking behavior and its state
dependence is also well discussed (Becker and Murphy, 1988; Gruber and Köszegi, 2001). The
findings of empirical research support the state dependence of smoking behavior based on analysis
using rational addiction models (Becker et al., 1994; Chaloupka, 1991; Labeaga, 1993; Yen andJones, 1996) and other models (Christelis and Sanz-de Galdeano, 2011; Gilleskie and Strumpf,
2005).
According to the literature, past smoking behavior affects current time preference and current
smoking behavior through its state dependence. Therefore, without controlling for the effect of pastsmoking behavior, the association between current time preference and current smoking behavior
is overestimated because of omitted variable bias; however, previous studies have not addressed thepresence of such omitted variable bias. In this study, to address the possibility of omitted variable
bias, I analyze the effect of current time preference on current smoking behavior by controlling
for the effect of past smoking behavior. To my best knowledge, this is the first study to verify
the effect of time preference on smoking behavior while controlling for the effect of past smoking
behavior. I use the Wooldridge conditional maximum likelihood (CML) estimator, which is one of
the estimators of dynamic panel models (Wooldridge, 2005). Additionally, I use the random effectprobit model in a static model (i.e., without controlling for the effect of past smoking status) tocompare the result with that derived from a dynamic model.
To track the transition of individual smoking behavior, I use the fourth wave of the Japan Household Panel Survey on Consumer Preferences and Satisfaction (JHPS) that contains nationwide representative micro panel data from Japan. The JHPS questionnaire includes questions on smoking
status, time preference, demographic variables, and other preferences. By using 5,568 observa-

2tions, I analyze the effect of current time preference on current smoking status. Some studies use
smoking status as a proxy for time preference or an instrument of time preference because measuring individual time preference is difficult (Fersterer and Winter-Ebmer, 2003; Munasinghe andSicherman, 2006). The present study also verifies the validity of using smoking behavior as a proxy
for time preference.
The findings of this study are threefold. First, as in the previous literature, without controlling
for the effect of past smoking behavior, I find positive associations between time preference andsmoking behavior, with the association significant for the male subsample. Second, by using a
dynamic panel analysis, I find that time preference affects smoking behavior significantly for thefemale subsample but not for the male subsample. This result is robust under several model specifications. Third, male smokers who have a low discount rate are more likely to want to quit smoking
or reduce smoking behavior, whereas female smokers with a low discount rate are no more likely
to want to quit smoking or reduce smoking behavior. This result implies that while male smokers
are concerned about the future consequences of smoking, they cannot manage to quit.
The remainder of this paper is organized as follows. In Section 2, I explain the data and show thesummary statistics. In Section 3, I discuss the Wooldridge CML estimator, which is an estimation
method for nonlinear dynamic panel models. I show the results in Section 4 and the robustness
check and discussion of the results are in Section 5. The conclusion is presented in Section 6.

2

Data and variables

I use the fourth wave (2009 to 2013) of JHPS data for the analysis in this study. Each survey iscollected during January and February of each year, and the sample size of each is approximately
4,000 to 6,000. The sampling procedure is designed to be representative. The purpose of the JHPS
is to verify the preference and utility assumptions applied in economics. Therefore, the questionnaires include questions on socioeconomic status and behavioral properties such as smoking status
as well as respondent preferences such as time and risk preferences. The questionnaire changes

3each year; some questions are added or deleted and the phrasing of some questions is changed.1
The fourth wave of the JHPS begins in 2009; however, the questions, particularly those on time
preference, are different before 2010 and after 2011. To avoid measurement error from the different questions, I thus use the data from 2011 to 2013 principally.2 I drop samples from the analysisof those who did not answer all the questions related to the estimation. In addition, because smokers are exposed to a greater health risk than nonsmokers, respondents who smoked in the previous
year, particularly older respondents, tended to be dropped from the survey. Doll et al. (2004) showthat the health risk for smokers aged over 55 years old is high. To address this sample selection
problem, I drop the sample that excludes 55-year-olds as well as older samples. Therefore, I use
5,568 observations.

2.1

Smoking behavior

The fourth wave of the JHPS asked respondents about their smoking behavior; however, the numberof possible responses to the question changed after the 2011 survey. In the 2010 survey, the choices
were the following: (1) Don’t smoke at all, (2) Hardly smoke, (3) Smoke sometimes, (4) About 10
cigarettes a day, (5) About a pack a day, (6) More than two packs a day, and (7) I used to smoke, buthave quit. After the 2011 survey, the number of possible responses increased to 10, and the choices
were as follows: (i) Never smoked, (ii) Hardly smoke, (iii) Occasionally smoke, (iv) I smoke about
one to five cigarettes a day, (v) I smoke about six to 10 cigarettes a day, (vi) I smoke about 11 to
20 cigarettes a day, (vii) I smoke about 21 to 30 cigarettes a day (viii), I smoke about 31 to 40
cigarettes a day, (ix) I smoke 41 cigarettes or more a day, and (x) I used to smoke, but I quit. For
the 2010 survey, I define respondents who choose (1) or (7) as nonsmokers and the others as current
smokers. In the 2011 to 2013 surveys, I define respondents who choose (i) or (x) as nonsmokers
and the others as current smokers. The definition of current smokers is relatively broad compared1

The data, questionnaire, sampling design, and details of the survey are available at http://www.iser.osakau.ac.jp/index-e.html.
2
I use smoking status in 2010 for the dynamic panel analysis and discuss the reason for this in Section 3.

4

with the literature to avoid the bias caused by changing the number of possible responses.3
Table 1: The frequency of current smokers

Total
Male
Female

2010

2011

2012

2013

706
(30.8)
484(46.6)
222(17.7)

562(27.8)
394(42.3)
168(15.5)

485(26.2)
339(40.1)
146(14.6)

432(25.4)
299(39.3)
133(14.1)

Note: The proportions of current smokers are reported inparentheses.

Table 1 shows the frequencies and proportions of current smokers for each year. Approximately
27% of the sample are current smokers, with the proportion of male smokers three times greaterthan that of female smokers in each year (41% for male and 15% for female).
Table 2 shows the pattern of smoking behavior for the sample in the sample period. In thefull sample, the majority—66%—are nonsmokers in all periods (In Table 2, “0000” represents thatgroup); by contrast, the second biggest group in the sample—20.7%—represents current smokers
in all periods (In Table 2, “1111” represents that group). This finding implies that smoking behavior
has strong persistence. The male and female subsamples show a similar pattern: The majority ofthe sample—49.6% in the male subsample and 79.2% in the female subsample—are nonsmokers
in all periods and the second biggest group—33.1% in the male subsample and 10.7% in the female
subsample—are current smokers in all periods.

2.2Time preference and control variables

Time preference is measured by using the multiple price list method for the questionnaires. This
popular method is used to elicit time preference. Respondents make several intertemporal decisions; they choose their preference between an immediate monetary reward, JPY X in t days, and3

For example, Christelis and Sanz-de Galdeano (2011) define daily smokers as current smokers and thus include
occasional smokers within nonsmokers.

5

Table 2: The pattern of smoking behavior
Smoking Pattern

Total

Male

Female

0000
0001
0010
0011
0100
0101
0110
0111
1000
1001
1010
1011
1100
1101
1110
1111

1334
17
8
11
19
2
6
21
85
9
4
21
24
12
30
419

447
6
3
8
12
2
2
11
42
7
3
14
17
10
19
299

887
11
5
3
7
0
4
10
43
2
1
7
7
2
11
120

N

2022

902

1120

Note: Each number in the “Smoking Pattern” columns represents personal smoking behavior from 2010 (left) to 2013(right); the number “0” indicates a nonsmoker and “1” indicates a smoker.

6a delayed reward, JPY Y in t + 7 days. With fixed t, respondents answered nine related questions
with varying values for X and Y. The annual interest rate of the delayed reward based on the immediate reward ranges approximately from −10% to 5111%. I expect respondents to answer thequestions according to their discount rate. Specifically, they choose the immediate reward if theirdiscount rate is higher than the interest rate of the question; otherwise, they choose the delayed
reward. Therefore, if a respondent switches from the immediate reward to the delayed reward, thenthe respondent’s discount rates are between the highest interest rate among the questions for which
they chose the immediate reward and the lowest interest rate among the questions for which theychose the delayed reward. For example, suppose a person chose the immediate reward when theinterest rate of the delayed reward was 0%, while the person chose the delayed reward when theinterest rate of the delayed reward increased to 10%. Then, the person’s discount rate is between
0% and 10%. Because the personal discount rate is not uniquely determined in this design, I use
the interval regression to calculate the expected discount rate. I drop the samples who chose thedelayed reward for a question but the immediate reward despite a higher interest rate than that of
the question as well as those who did not answer all the questions.
The JHPS prepared two conditions for t: today and in 90 days. I elicit respondents’ discountrates in each condition and combine two of the standardized discount rates. I then use the combined
discount rate as the personal discount rate, Discount, in this analysis.4
The JHPS also asked respondents to describe their risk preference. They were asked whether
they would buy a lottery ticket with a 50% chance of winning JPY 100,000 at a certain price.
Respondents answered eight related questions that provided a variety of prices for the lottery ticket,
ranging from JPY 10 to JPY 50,000.5 The estimation method is the same as that used for the time
preference analysis. I follow Cramer et al. (2002) approach to compute the degree of absolute risk
aversion. Then, by using the range of the logarithm of the degree of absolute risk aversion, I use
the expected logarithm of absolute risk aversion from the result of the interval regression. I also
4

The interest rates of the questions are not the same within the surveys. I thus adjust the interest rates to address themeasurement error.
5
Precisely, the prices of the lottery tickets were JPY 10, 2,000, 4,000, 8,000, 15,000, 25,000, 35,000, and 50,000.

7

drop the samples who chose to buy a lottery ticket at a certain price but who decided not to buy alottery when the price fell.
In addition to risk preference, I use control variables to identify the effect of time preference on
smoking behavior: age, squared age, a dummy for education, marital status, a dummy for those withchildren, categorical dummies for household income, a dummy for sex, year dummies, occupation
dummies, self-assessed stress level, and a dummy for negative change in self-assessed health fromthe last year.678
Table 3 shows the summary statistics of time preference and the control variables according tosmoking status. The differences in preferences and characteristics by smoking status are observed
from a mean comparison test. Current smokers have a higher discount rate than nonsmokers.
Income and education level are lower for current smokers than nonsmokers.
Figure 1 shows the within difference of Discount. Time preference for the most of the sample
does not vary by over one-half unit of standard deviation in one year. The samples who did notsmoke in the last year but smoke in the year of the survey (In Figure 1, “01” represents that group)
tend to decrease their discount rate from the last year. On the contrary, the samples who smoked in
the last year but do not smoke in the year of the survey (In Figure 1, “10” represents that group),
particularly female samples, tend to increase their discount rate from the last year.
Table 3 and Figure 1 therefore imply that time preference is associated with smoking behavior
and that past smoking behavior affects current time preference. I research these relationships more
precisely by using nonlinear panel analysis.
6

Cigarette tax increased in October 2010. This price effect is therefore captured by the year dummies. Further,
excise tax increased in April 2014 (the announcement was October 2013). Therefore, this increase in excise tax does
not affect my sample because surveys were collected during January and February.
7
Lerner et al. (2012) show that stress increases the discount rate.
8
Sundmacher (2012) shows that negative health shocks affect smoking behavior as well as change time preference
according to the endogenous time preference model. The dummy for negative health shocks thus captures the third
effect.

8

Table 3: Summary statistics
Male
Nonsmoker Smoker
Discount
Riskaverse
Age
IncomeEducation
MarryChildren
Health change
StressN

FemaleNonsmoker Smoker−0.09(0.99)−11.31(1.66)
43.78(7.55)
4.94(1.81)
0.59(0.49)
0.76(0.43)
0.5
(0.5)
0.27
(0.44)
2.6(1.02)

0.1∗∗∗(1.15)−11.21(1.29)
43.8(7.36)
4.72∗∗∗(1.84)
0.49∗∗∗(0.5)
0.76(0.43)
0.51
(0.5)
0.24∗
(0.43)
2.61(1.03)−0.24(0.92)−11.71(1.8)
43.31(7.59)
4.81(1.82)
0.59(0.49)
0.78(0.41)
0.55
(0.5)
0.24
(0.43)
2.57(1.03)

0.02∗∗∗(1.16)−11.58(1.7)
42.97(6.84)
4.3∗∗∗(1.86)
0.31∗∗∗(0.46)
0.77(0.42)
0.58(0.49)
0.25(0.43)
2.48(1.09)

1507

1032

2583

447

Note: Standard deviations are reported in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, and ∗∗∗ p < 0.01 denote the statistical significanceof the difference of nonsmokers.

9Figure 1: The within difference of DiscountMale

FemaleNote: Each number in the legends represents personal smoking behavior in the last year (left) and in the year of thesurvey (right); the number “0” indicates a nonsmoker and “1” indicates a smoker.
Note that the sample period in this figure is only from 2012 to 2013 because individuals’ time preferences are notelicited in the 2010 survey.

10

3

Estimation method

In this section, I explain the estimation method used in this study. Consider a latent model with
AR(1). The latent variable for individual i at time t is as follows:~ it ~β + ci + uit ,y∗it = ρyi,t−1 + βDiscountit + X(1)

where yi,t−1 is the observed variable for individual i at time t − 1; Discount is the standardized~ it is the control variable; ci is unobserved individual heterogeneity, and uit is thediscount rate; X
error term. The relationship between latent variable y∗it and observed variable yit is as follows:




∗



1 if yit > 0,
yit = 
0 otherwise.

(2)Then, the conditional joint probability of the observed variable can be written as follows:
 T


 Y



~ i , ci = ~ it , ci  P(yi0 |X~ i , ci ).
P yi0 , yi1 , ..., yiT |X
P yit |yi,t−1 , X

(3)

i=1

If the initial value yi0 and unobserved individual heterogeneity ci are independent, then ci can be
integrated out from the above equation by using numerical integration; hence, the random effectprobit estimation is valid for consistency under the assumption that the error term and unobserved
individual heterogeneity follow a normal distribution. In the context of smoking behavior, unobserved individual heterogeneity can arise through the linking of smoking, preferences, and theenvironment; therefore, the independence assumption between the initial value and unobserved individual heterogeneity is implausible. Wooldridge (2005) proposes using the estimation method if
the independence assumption is violated, suggesting modeling unobserved individual heterogeneity given the initial value instead of modeling the initial value given unobserved heterogeneity.
Wooldridge (2005) also proposes that assuming unobserved individual heterogeneity given the initial value follows a normal distribution. Therefore, unobserved individual heterogeneity decom11

poses into two parts: one part is correlated with the initial value and the other part is independentof the initial value. Then, unobserved individual heterogeneity can be rewritten as follows:ci = α0 + α1 yi0 + αi , αi ∼ N(0, σ2α ).

(4)

By substituting the above equation into equation (1), the latent variable for individual i at time t
can be rewritten as follows:~ it ~β + α0 + α1 yi0 + αi + uit .
y∗it = ρyi,t−1 + βDiscountit + X(5)Then, the random effect probit estimation using equation (5) is valid. I use smoking status from the2010 survey, Smoke2010, as the initial value.

4

Results

This section presents the estimation results. All the estimations are run separately by gender. Ichange the coefficient to the average partial effect in all results. Table 4 shows the average partial
effects in the static and dynamic models.
In all models, the individual discount rate, Discount, is positively associated with the probability of smoking; these results are consistent with the hypothesis that current smokers place minimalvalue on the future consequences of smoking. In the static models, model (1), the significant association between the discount rate and smoking behavior in the male subsample is observed; however,no significant association between those in the female subsample is observed. These results are
consistent with those of Harrison et al. (2010) and Kang and Ikeda (2014). Quantitatively, a oneunit increase in the standard deviation of the discount rate increases the probability of smoking
by approximately 3.7 percentage points for the male subsample and 0.02 percentage points for thefemale subsample.
In the dynamic model, model (2), the discount rate affects smoking behavior insignificantly for12Table 4: The average partial effects on smoking
Male
StaticDynamic(1)(2)
Discount

0.037∗∗(0.016)0.002(0.002)

FemaleStaticDynamic(1)(2)
0.0002(0.0002)0.010∗∗∗(0.003)

Smoke at t-1

0.018(0.013)0.100∗∗(0.039)

Smoke2010

0.146∗∗∗(0.046)

0.098∗∗∗(0.018)

N
AIC
σα

2538
980.7
1.834

2538
1307.02.001

3030
1489.8
5.969

3030
939.6
0.756

Note: Standard errors in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
σα is the standard deviation of unobservable individual heterogeneity.

the male subsample but significantly for the female subsample. Qualitatively, a one-unit increase in
the standard deviation of the discount rate increases the smoking probability by approximately 0.2
percentage points for the male subsample and approximately 1.0 percentage point for the female
subsample. In contrast to the male subsample result and hypothesis from the endogenous time preference model, the coefficient of Discount in the female subsample is higher than that in the static
model. One explanation for this result for the female subsample is the difference in the variancein unobserved individual heterogeneity between the static and dynamic models. The difference inthe estimated standard deviation of unobserved individual heterogeneity in the female subsample
is large: about 5.97 in the static model and 0.76 in the dynamic model. However, the difference inthe estimated standard deviation of unobserved individual heterogeneity in the male subsample issmall: about 1.83 in the static model and 2.00 in the dynamic model. Therefore, the large standard
deviation of unobserved individual heterogeneity pushed down the magnitude of Discount in thestatic model.
The state dependencies of smoking behavior are observed for the female subsample but notfor the male subsample. The coefficients of smoking status in the initial period, Smoke2010, are13

significant for both genders. This finding implies that unobserved individual heterogeneity is notindependent of smoking status in the initial period.9

5

Robustness check

In this section, I check the robustness of the results presented in the previous section and discuss
the results.

5.1

Former smokers

Table 4 shows that unobserved individual heterogeneity is related to smoking status in the initialperiod. In the estimation in Table 4, smoking status in the initial period is categorized according
to current smokers and nonsmokers. Moreover, nonsmokers can be separated into two types: those
who have never smoked and former smokers. It is plausible that the unobserved individual heterogeneity of former smokers differs from the unobserved individual heterogeneity of those who havenever smoked. Because respondents who answered question (1) concerning their smoking status
in the 2010 survey or (i) in the 2011 to 2013 surveys can be regarded as having never smoked, I
separated nonsmokers into those who have never smoked and former smokers; in the initial period, 25.9% of nonsmokers in the whole sample, 47.9% of nonsmokers in the male subsample, and14.1% of nonsmokers in the female subsample were former smokers. In the estimation, I include adummy for former smokers in the initial period, Smoked2010, in the dynamic model, and the result
is reported in Table 5. The coefficients of Smoked2010 are significant for both the male subsample
and the female subsample. This finding implies that unobserved individual heterogeneity is also
9

I also run the estimation by using the Chamberlain–Mundlak (Chamberlain, 1980; Mundlak, 1978) approach that
includes the within mean of the explanatory variables to capture the correlation between the explanatory variables
and unobserved individual heterogeneity. The result is similar except for the insignificant effect of time preference on
smoking behavior in the static model for the male subsample. Some studies argue that time preference is correlatedwith unobserved individual heterogeneity such as intelligence (Shamosh and Gray, 2008). However, Burks et al.
(2012) show an association between time preference and smoking behavior when IQ score is controlled for. As shownin Figure 1, the within variation of time preference is not large. Therefore, the difference in the result from Table 4 inthe static model for the male subsample is caused by the insufficient variation in time preference rather than omitted
variable bias.

14different between those who have never smoked and former smokers. The other results are similar
to those in Table 4.
Table 5: The results for former smokers
Male

FemaleDiscount

0.002(0.002)0.010∗∗∗(0.003)

Smoke at t-1

0.022(0.017)0.101∗∗∗(0.034)

Smoke2010

0.197∗∗∗(0.063)0.125∗∗∗(0.026)

Smoked2010

0.056∗∗(0.023)0.065∗∗∗(0.012)

N
AIC
σα

2538
1284.5
1.915

3030
900.2
0.757

Note: Standard errors in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
σα is the standard deviation of unobservable individual heterogeneity.

5.2

Strength of state dependence

The JHPS asked a question concerning the consumption of smokers. The extent of the state dependence of smoking may depend on cigarette consumption, while the effect of past smoking statuson time preference also depends on past cigarette consumption. To address the strength of state
dependence, I separated the lag of smoking status into three variables by cigarette consumption.
Respondents who answered question (6) in the 2010 survey, or either (viii) or (ix) in the 2011 to2013 surveys, are considered to have high consumption. Those who answered questions (4) or(5) in the 2010 survey, or (v), (vi), or (vii) in the 2011 to 2013 surveys, are considered to havemedium consumption. Those who answered questions (2) or (3) in the 2010 survey or (ii), (iii),or (iv) in the 2011 to 2013 surveys, are considered to have low consumption. The variables Highsmoker, Medium smoker, and Low smoker correspond to the dummy variables for high, medium,15and low consumption, respectively. Table 6 shows that the share of the high consumption group isless than 10%, that of the medium consumption group is approximately 70% to 80%, and that of
the low consumption group is approximately 15% to 25%. I then separate the initial period dummy
into three dummy variables according to smoking behavior in the initial period, Low smoker 2010,
Medium smoker 2010, and High smoker 2010, as well as include a dummy for former smokers in
the initial period Smoked2010.
Table 6: Share of cigarette consumption
2010

2011

2012

2013

Total
High smoker
Medium smoker
Low smoker

4.1
74.4
21.5

4.8
69.6
25.6

5.4
76.9
17.7

6.0
75.9
18.1

Male
High smoker
Medium smoker
Low smoker

5.2
77.5
17.4

6.3
71.1
22.6

7.1
77.6
15.3

7.0
74.9
18.1

FemaleHigh smoker
Medium smoker
Low smoker

1.8
67.6
30.6

1.2
66.1
32.7

1.4
75.3
23.3

3.8
78.2
18.0

Consistent with my hypothesis, the strength of state dependence increases with cigarette consumption (Table 7). Further, state dependence is observed for the male subsample except for low
consumption at t−1. Because the number of high-consuming female smokers is small, High smoker
at t − 1 is not statistically significant. Therefore, state dependence exists for both the male and thefemale subsamples; however, the extent of state dependence for male is lower than that for female.
The other interpretations of the results are the same as those in Table 4.

5.3

Present bias

Behavioral economists argue that time preference for some individuals does not hold stationarity;
people also distinguish between today and later and discount later utility additionally. This model
16

Table 7: Strength of state dependence

Discount
High smoker at t-1
Medium smoker at t-1
Low smoker at t-1
High smoker 2010
Medium smoker 2010
Low smoker 2010
Smoked 2010
N
AIC
σα

Male

Female0.002(0.003)0.073∗∗(0.037)0.041∗∗(0.019)0.011(0.014)0.257∗∗∗(0.042)0.262∗∗∗(0.030)0.196∗∗∗(0.026)0.074∗∗∗(0.019)0.010∗∗∗(0.003)0.034(0.059)0.090∗∗∗(0.032)0.060∗∗(0.026)0.185∗∗∗(0.059)0.129∗∗∗(0.021)0.090∗∗∗(0.017)0.058∗∗∗(0.011)

2538
1233.6
1.783

3030
838.0
0.909

Note: Standard errors in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
σα is the standard deviation of unobservable individual heterogeneity.

17

is called a quasi-hyperbolic time preference model. According to the model, time preference can beseparated into two parts: one part is the discount rate, which is used in economics, and the other ispresent bias, which distinguishes between now and later (Gruber and Köszegi, 2001; Laibson, 1997;
O’Donoghue and Rabin, 1999, 2001). In the JHPS questionnaire, with respect to time preference,
two conditions relate to the timing of the receipt of an immediate reward: today and 90 days fromtoday. Therefore, present bias can be captured if the discount rate is measured by using the questionon whether the timing of the receipt of an immediate reward today is higher than that in 90 days.
Hence, I include a dummy for present bias, Present bias, in the models.
Table 8: Quasi-hyperbolic time preference model

Static(1)

Male
Dynamic(2)

FemaleStaticDynamic(1)(2)

Discount

0.034∗∗(0.016)0.002(0.003)0.0002(0.0005)

0.102∗∗∗(0.003)Present bias

0.0002(0.0039)−0.002(0.006)−0.00002(0.00008)

0.008(0.009)

Smoke at t-1

0.038(0.024)0.135∗∗∗(0.033)

Smoke2010

0.172∗∗∗(0.040)0.083∗∗∗(0.015)

N
AIC
σα

2165
197.8
1.939

2165
1091.61.859

2561
1324.4
7.275

2561
787.1
0.407

Note: Standard errors in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
σα is the standard deviation of unobservable individual heterogeneity.

Table 8 shows that present bias is not significantly associated with smoking behavior in allmodels, while the other findings for time preference and the state dependence of smoking are thesame as those in Table 4.10
10

As in Kang and Ikeda (2014), Present bias may have measurement error. To address the measurement error,
according to Kang and Ikeda (2014), I created a proxy for present bias by using a retrospective question concerning
the completion of childhood assignments in the 2013 survey: When you were a child and were given an assignment
during the school vacation, how early did you usually finish the assignment? (i) Got it done right away, (ii) Tended to

18

5.4

Smoking intention

From the results in Table 4 for the male subsample, time preference is significantly associated withsmoking behavior in the static model, but not in the dynamic model (i.e., when I control for the effect of past smoking behavior). This result can be interpreted as that male smokers are unconcerned
about the future consequences of smoking behavior. However, Grignon (2009) reports that 57% of
current smokers fail to quit smoking. Kan (2007) also shows that current smokers who want to quit
are more likely to support tobacco control policy. Therefore, an alternative interpretation is thatmale smokers who have a low discount rate want to quit smoking but cannot manage to do so. I
test the latter interpretation next.
In 2012 and 2013 surveys, the JHPS asked about smokers’ intention to quit. The choices wereas follows: (i) I want to quit smoking, (ii) I want to reduce the amount of cigarettes I smoke, (iii)
I don’t want to quit smoking, (iv) I don’t know, and (v) I don’t smoke. Table 9 shows the result ofa probit analysis of intention to quit smoking for current smokers. The dependent variable is thedummy for wanting to quit smoking or reduce tobacco consumption (answering questions (i) or(ii)). The results show that male smokers who have a lower discount rate are more likely to wantto quit smoking or reduce tobacco consumption than those who have a higher discount rate. On
the contrary, female smokers with a low discount rate are no more likely to want to quit smoking
or reduce tobacco consumption. 11 This result implies that male smokers are concerned about the
future consequences of smoking, but cannot manage to quit. Hence, providing commitment tools
such as those proposed by Giné et al. (2010) may work for male smokers.
get it done early, before the due date, (iii) Worked on it daily up until the due date, (iv) Tended to get it done toward
the end, (v) Got it done at the last minute, (vi) Got it done after the deadline, (vii) Did not do it, and (viii) There wasno assignment. I created an indicator variable as a proxy for present bias, which takes one if the answer was (iv),(v), (vi), or (vii) and zero otherwise. I dropped samples who answered (viii). Although Kang and Ikeda (2014) finda strong relationship between the proxy of present bias and smoking behavior, I find no such statistically significant
relationship.
11
A weakly significant association between time preference and the intention to quit smoking was observed for thefemale subsample when the quasi-hyperbolic time preference model was used. Moreover, present bias was negatively
statistically significant.

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Table 9: Intention to quit smoking

Discount
N
AIC

Male

Female−0.035∗∗(0.017)−0.037(0.026)

624
824.6

251
296.3

Note: Standard errors in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

6Conclusion

In this study, I analyzed the effect of time preference on smoking behavior while controlling forthe effect of past smoking behavior, using a dynamic panel analysis. I also compare the resultswith those derived by adopting a static model. The findings of this study are threefold. First, as in
the previous literature, without controlling for the effect of past smoking behavior, I find positive
associations between time preference and smoking behavior, with the association significant forthe male subsample. Second, by using a dynamic panel analysis, I find that time preference affects
smoking behavior significantly for the female subsample but not for the male subsample. This result
is robust under several model specifications. Third, male smokers who have a low discount rate are
more likely to want to quit smoking or reduce smoking behavior, whereas female smokers with alow discount rate are no more likely to want to quit smoking or reduce smoking behavior. This
result implies that while male smokers are concerned about the future consequences of smoking,they cannot manage to quit. In addition, providing a commitment tool such as that proposed byGiné et al. (2010) may work for male smokers.
This study has some limitations. First, the sample period is relatively short and thus a more
precise estimation of state dependence is needed. Second, this study does not focus on the effectof increasing tobacco prices on people who want to quit smoking. Future research is therefore
required to tackle these limitations.

20

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