GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.142

Time Discounting and Smoking Behavior under Tax Hikes

Myong-Il Kang and Shinsuke Ikeda

June 2010

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Time Discounting and Smoking Behavior under Tax Hikes*

Myong-Il Kang1and Shinsuke Ikeda2
Korea University
ISER, Osaka University

June 17, 2010*

Our special thanks go to Y. Fukuta, M. Nakagawa, F. Ohtake, J. Wan, and participants at the International

Workshop on the Economics of Obesity and Health 2009 and the 69th International Atlantic Economic
Conference 2010 for helpful comments. We acknowledge financial supports from: the COE and Global COE
Programs of Osaka University; a Grant-in-Aid for Scientific Research (B 21330046) from the Japan Society for
the Promotion of Science; and the Joint Usage/ Research Center Projects.
1

Corresponding author: M. Kang, Department of Business Administration, Korea University, 1-700

Ogawa-cho Kodaira, Tokyo 187-8560, Japan. Tel.: 81-42-341-1331. E-mail: <mkang@korea-u.ac.jp>
2

S. Ikeda, The Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka, Ibaraki, Osaka

560-0047, Japan. Tel.: 81-6-6879-8568. E-mail: <Ikeda@iser.osaka-u.ac.jp>

Abstract

By combining our broad panel survey of Japanese adults from 2005 to 2008 and actual cigarette tax data,
we investigate how smoking behavior including responses to tax hikes depends on time discounting and its
biases, such as hyperbolic discounting and the sign effect. Cigarette consumption displays significantly
positive correlations with discount rates and the procrastinating tendency, and negative correlations withthe sign effect. Hyperbolic, procrastinating, and naïve respondents decrease their after-tax-hike cigarette
consumption more than the others, implying that, irrespective of the preannouncement of a future tax hike,they postpone smoking moderation until the tax hike actually takes place. Finally, the government’s
revenue from cigarette tax peaks at a JPY 29.92 (around USD 0.28 using the conversion rate [107.16] inFebruary 2008) higher tax per cigarette than the present actual level.

Keywords: smoking, cigarette tax, time preference, discount rate, hyperbolic discounting, procrastination,
the sign effect

JEL classification: I18, Z00

1.

Introduction

By analyzing our broad survey of Japanese adults from 2005 to 2008, the purpose of this paper is to
examine empirically the effects of time discounting and its behavioral biases on smoking behavior, andtheir responses to a hike in cigarette taxes. We incorporate two time discounting biases that has beenreported in dynamic choice theory: (i) hyperbolic discounting, under which a person discounts theimmediate future more intensively than the distant future, and thereby makes time-inconsistent decisions;and (ii) the sign effect, by which a person discounts positive future payoffs more intensively than negative
future payoffs (for the empirical validity of the biases and their implications, see, e.g., Thaler, 1981;
Benzion et al., 1989; Prelec and Loewenstein, 1991; Ainslie, 1992; Khwaja et al., 2007). We hypothesizethat hyperbolic discounters smoke more than exponential discounters; that reductions in cigarette
consumption after a tax hike amongst naïve hyperbolic smokers are larger than those amongst the others;
and that decreases in cigarette consumption after a tax hike amongst hyperbolic smokers are larger thanthose amongst exponential smokers.
The novelty of the present research is in the following two points. First, we identify respondents’ types
by whether discounting is exponential or hyperbolic and by whether decision making is sophisticated ornaïve. We estimate differences in smoking behavior among the distinct types of respondents. Second, byusing the actual time series data of the cigarette tax, which is exogenously determined by the Japan’s
government, we quantify how the natural experiment of cigarette tax hikes in 2006 affects smoking
behavior of each type of respondents. To do so, we use our four-year panel survey data, in which roughly
three to five thousands of Japanese adults each year reply to questionnaires on preferences and various
attributes.
The main results of this article are as follows: Firstly, discount rates and time discounting biases relate
to cigarette consumption. Discount rates, the procrastinating tendency, and naiveness have significantly
positive associations with cigarette consumption and the sign effect has significantly negative correlations
to smoking behavior. Secondly, tax hikes decrease ex-post smoking. In particular, hyperbolic,procrastinating, or naïve respondents decrease their after-tax-hike cigarette consumption more than others,

1implying that they postpone smoking moderation in response to future tax hikes and thereby adjust
cigarette consumption mainly after the tax hike. Thirdly, other factors, e.g., gender, degree of risk aversion,education, household income, age, and existence of children significantly relate to smoking behavior.
Finally, the government’s revenue from cigarette tax peaks at a JPY 29.92 (around USD 0.28 using theconversion rate [107.16] in February 2008) higher tax per cigarette than the present actual level.
In the present research, we follow Ikeda et al. (2010) in constructing data for discount rates, hyperbolic
discounting or procrastination, and the sign effect from response data to questions on various hypotheticalintertemporal decisions including choices between a small amount of present money and a larger amountof future money. In addition, naïve and sophisticated respondents are identified by using response data to
two questions regarding procrastination in doing homework assignments: one regarding when they used toplan to do homework in summer vacations in their school days; and the other regarding when they in effectused to do homework. Respondents are classified as naïve if they used to procrastinate doing homework
more than they planned whereas they are classified as sophisticated otherwise.
There are many empirical studies which investigate smoking behavior from the viewpoint ofintertemporal decision makings (e.g., Mitchell, 1999; Bickel et al., 1999; Odum et al., 2002; Baker et al.,
2003; Sato and Ohkusa, 2003; Reynolds et al., 2004; Ohmura et al., 2005; Khwaja et al., 2007; Ida andGoto, 2009) and by using rational addiction models (e.g., Chaloupka, 1990, 1991; Keeler et al., 1993;
Becker et al., 1994; Bardsley and Olekalns, 1998; Luo et al., 2003; Wan, 2006). Despite many attempts,
however, the existing literature has not incorporated the effects of time discounting biases that behavioral
economics has been reporting as important determinants of seemingly irrational behaviors such as
time-inconsistent impulsive consumption of drugs and cigarettes (see, e.g., Ainslie, 1992).
Our research is closely related in spirit to Gruber and Koszegi (2001), which quantified by calibrations
the welfare–enhancing effects of cigarette taxation under hyperbolic discounting. Although theyemphasized the critical role that time-inconsistent preferences played in smoking-decision making, fortheir result to be empirically valid, it should be empirically examined how hyperbolic discounting affects
smoking behavior, including responses to tax hikes, and how cigarette consumption differs between naïve
and sophisticated smokers.1

1

In pararel with our research, Ida (2009) examines correlations between implusiveness (hyperbolic

2The remainder of the paper is structured as follows: in section 2, our hypotheses of relations between
time preferences and smoking are discussed. Section 3 presents a description of the data. Section 4 reports
the estimation results about the determinants of smoking. Section 5 shows the differences of responses totax hikes among types of time discounting and presents the policy implications of tax hikes. Finally,
section 6 concludes the paper.

2. Time discounting and smoking

2.1. Discount rates

As shown by the theory of rational addiction (Becker and Murphy, 1988), persons with high discount rates
can raise present utility by consuming addictive goods even if it induces future disutility by accumulating
addiction, and thus they would smoke more than others. Indeed, significantly positive correlations between
the amount of smoking and discount rates has been reported in literature (e.g., Bickel et al., 1999; Mitchell,
1999; Odum et al., 2002; Baker et al., 2003; Sato and Ohkusa, 2003; Reynolds et al., 2004; Ohmura et al.,2005; Ida and Goto, 2009). Therefore, it can be hypothesized that persons with higher discount rates tend
to smoke more than others.

2.2. Hyperbolic discounting

Hyperbolic discounting, under which a person discounts the immediate future more intensively than the
distant future, induces the present bias, and thereby likely lead consumers to smoke excessively by
underweighting the resulting future disutility. Sophisticated hyperbolic discounters, who are well aware of
their procrastinating tendency due to hyperbolic discounting, smoke time-consistently by choosing

discounting) and smoking by eliciting discount rates and risk preferences simultaneously from cross-section data.
He does not distinguish sophisticated and naïve respondents, incorporate sign effects, nor analyze the effect oftax hikes.

3sequences of such high cigarette consumption levels that future selves would also choose under the same
present bias. On the other hand, naïve hyperbolic discounters, who are unaware of their hyperbolic
discounting, do not take into account their time-inconsistency problem in the smoking decision andtherefore, smoke more than the sophisticated.
From the same logic, when the cigarette tax rises, hyperbolic discounters are likely to reduce cigarette
consumption later than exponential discounters. Insofar as hyperbolic discounters are forward-looking
optimizers, they start decreasing cigarette consumption when the preannouncement of a future tax hiketakes place. Sophisticated hyperbolic discounters, however, choose such a small decrease in earlier periodthat future selves can consistently support. It is only after the tax and hence the cigarette price actuallyrises when they decrease cigarette consumption largely. In sum, sophisticated hyperbolic discountersprocrastinate to moderate cigarette consumption until the tax hike is actually introduced. Naïve hyperbolic
discounters procrastinate time-inconsistently the moderation of the consumption with a high discount rate
for immediate future disutility. Thus, we hypothesize that hyperbolic discounters tend to smoke more than
exponential discounters, and that even when a tax hike is pre-announced, they are likely to moderate
cigarette consumption substantially after the tax hike is actually implemented.2

2.3. The sign effect

The sign effect, by which a person discounts positive payoffs more intensively than negative payoffs, leads
to moderation in current addictive consumption because future disutility through the harmful effects ofaddiction is overvalued (e.g., Baker et al., 2003). Therefore, we hypothesize that person with the sign

2

In the context of rational addiction, hyperbolic agents behave forward-lookingly take into account future

utility/disutility of addictive consumption (Gruber and Koszegi, 2002). However, it is difficult to analyticallyshow whether hyperbolic discounters genarally smoke more than exponential discounters. Laibson (1997, 1998)
shows that, under certain conditions, the effective discount rate which is obtained by transforming thehyperbolic discounting function into the exponential one is greater than the pure exponential discount rate.
Accordingly, we hypothesize that persons with hyperbolic discounting or procrastination tend to smoke morethan ones with exponential discounting.

4

effect smoke less than others.

3. The data

Our empirical research is based on the annual panel data of the Japan Household Panel Survey on
Consumer Preferences and Satisfaction (hereafter, JHPS) from 2005 to 2008. Table1 summarizes thenumber of samples and respondents. The JHPS was set up in 2004 as a project of the COE program in
Osaka University using a random sample by a placement method and it had 4,224 respondents in 2004 and
2,987 in 2005. Excluding 108 individuals lost from the respondents in 2005, 2,000 new random-sampledindividuals were added to the survey sample, and 3,763 individuals responded in 2006 and 3,112 in 2007.
The number of respondents was 4,018 in 2008, with 2,731 being continuous respondents from the previous
year and 1,287 mailing their poll responses. All surveys were conducted in February, except in 2006 when
the survey was conducted in February and March.

Insert Table 1:
The number of samples and frequencies of responses

3.1. Tax hikes and cigarette consumption

3.1.1. Cigarette consumption

The JHPS asks respondents about their smoking habits saying, “How many cigarettes do you smoke
regularly? Select a proximal option from the following; (i) Never smoke at all; (ii) Hardly smoke; (iii)
Smoke sometimes; (iv) About 10 cigarettes a day; (v) About a pack a day; (vi) More than 2 packs a day.”In 2007 and 2008, option “(vii) I used to smoke but have quit” is added.
To quantify cigarette consumption, we categorize respondents who select option (i), (ii) or (vii) as
nonsmokers, (iii) as smokers consuming 0 to 5 cigarettes a day, (iv) 5 to 15, (v) 15 to 40, and (vi) as more

5

than 40. By using the category data, we apply the method developed by Kimball et al. (2005) to estimate alog normal distribution for the distribution of the respondents’ cigarette consumption of each year, fromwhich each respondent’s cigarette consumption is estimated as an expected value conditional on his or her
categorical level of smoking.

Insert Table 2:
Summary statistics of smoking behavior

Insert Figure 1:
Transition graphs of smoking behavior

The summary statistics of cigarette consumption and smoking rates are shown in Table 2 and thetransition graphs are described in Figure 1, where SMOKING represents the cigarette consumption
estimated above. Male smokers smoke more than a pack per day and the amounts of smoking and smoking
rates decreased during the entire investigation. On the other hand, the reported smoking amounts forfemales are around 15 per day and the amounts increased over time from 2005 to 2007 and decreased in
2008, although their smoking rates decrease throughout the entire period. These trends are consistent withthe reported data of the National Survey of Health and Nutrition (NSHN) conducted by the Ministry of
Health, Labour and Welfare, Japan: According to NSHN data from 2004 to 2005, the smoking amounts formale smokers decreased from 21.5 to 21.0 per day and the rates of regular smoking declined from 43.3%
to 39.3%. As for females, smoking amounts increased from 14.6 to 15.6 per day and their rates of regularsmoking decreased from 12.0% to 11.3%.

3.1.2. Tax hikes

We can examine the effect of the tax hike on smoking behavior because a change of taxation policy on
cigarettes arose during our sample period. The Japanese Government raised the cigarette tax from JPY
7.892 (around USD 0.074 using the conversion rate [107.16] in February 2008) to JPY 8.744 (around USD

60.082) per cigarette on July 1st, 2006.3 Our survey in 2006 was conducted in February, which is after the
Cabinet meeting on January 17th adopted the outline of revisions in the taxation system, and before theDiet on March 27th that passed the bill.
Our data are not frequent enough to detect the preannouncement effect of tax hikes unlike in Becker etal. (1994) which use monthly data. Therefore we limit our attention to smoking moderation after the tax
hikes.4 To examine post-tax hike behavior, we define a tax variable, named TAX, which represents the tax
levels in each year.

3.2. Eliciting discount rates and its behavioral biases

In the JHPS, the respondents' discount rates were measured by asking five questions about intertemporalchoice under alternative conditions. As in the previous survey research (e.g., Harrison et al., 2002; andBorghans and Golsteyn, 2006), the respondents were told to choose a preferable option from two options“A” and “B,” e.g., “A” receiving JPY 10,000 (around USD 93.32) in two days; and “B” receiving JPY
10,000 plus a certain amount of JPY α, say JPY 10,038 (around USD 93.67), in nine days, where choosing
the delayed receipt “B” instead of “A” implies receiving 20% of the annual interest rate. In each question,eight such problems were posed in the form of a payoff table, with alternative α values, from small to large,
and hence with alternative imputed interest rates, from low to high.
Table 3 shows QUESTION 1, where the amount of receipt for option “A” is specified as JPY 10,000
and the imputed interest rate for option "B" changes from -10% to 300%. Respondents are expected tochoose option “A” at low interest rates whereas, as the imputed interest rate goes up, to switch to “B” atsome critical high rate. The individual respondents' discount rates can be inferred by estimating the interest
rate at which the delayed receipt of “B” is indifferent to the more immediate receipt of “A”. The elicited
discount rates are associated with the particular choice conditions, e.g., two days vs. nine days, JPY 10,000
3

The price of Mild Seven Super Lights, which has hold the largest share of the Japanese market, has been risen

from JPY 270 (around USD 2.52) per 20-cigarette pack to JPY 300 (around USD 2.80) following the tax hike.
4

Announcement of the Cabinet approval for the still unpassed bill is weak as information about the tax hike,and the date when our survey was conducted, Feb., 2006, is far from the execution date, Jul. 1st, 2006.

7

for option “A” in QUESTION 1.

Insert Table 3:
Question to elicit discount rates: An example (QUESTION 1 for DR₁)

To test for time discounting biases, five questions were designed by controlling for: (i) money amounts
for option “A” as JPY 10,000 or JPY 1 million (around USD 9331.84); (ii) time horizons for “A” as twodays, one month, or ninety days; (iii) time delays as seven days or 12 months; and (iv) receipt or payment.
In “payment” question 5, the respondents were asked to choose: “A” paying JPY 1 million in one month;or “B” paying JPY 1 million plus some amount in 13 months, from which acceptable interest rate
payments to delay a JPY 1 million payment for 12 months were measured.
From each question, we obtaine raw response data in the form of category numbers, which tell us
between which interest rates each respondent's choice switched from option “A” to “B” if any switch takes
place. Some subjects did not switch their choices from “A” for all offered interest rates. By using the raw
category data, we apply the method developed by Kimball et al. (2005) to estimate a log normal
distribution for the cross-respondent distribution of gross discount rates, from which each respondent's
gross discount rate for a certain question, i.e., a certain payoff table, is estimated as an expected value
conditional on his or her switch taking place at a certain observed category. Descriptive statistics of elicited
discount rates, together with the choice conditions under which they were elicited, are summarized in Table
4, where DRi ( i

1,L, 5 ) represents the discount rate estimated from question i.

Insert Table 4:
Elicited discount rates under alternative choice conditions

To test for the effect of impatience on cigarette consumption, we construct DISCRATE, which
represents the simple average of the standardized values of the elicited discount rates DRi ,t ( it2005,L , 2008 ):8

1,L, 5 ,DISCRATEt
where E t ( DRi ,t ) andt(1 / 5)5i 1( DRi ,tEt ( DRi ,t )) /t

( DRi ,t )(1)

( DRi ,t ) represent sample means and standard deviations of DRi ,t (see table4). It is hypothesized that, with other things being equal, the respondents’ cigarette consumption depends
positively on the impatience index DISCRATE.
Table 5 shows the simple correlation of time discounting variables with SMOKING.. Consistent with
our hypothesis mentioned above, column 1 shows a significantly positive correlation betwwwn
DISCRATE and cigarette consumption. 5

Insert Table 5:
Correlation coefficients of time discounting variables with SMOKING

Table 6 summarizes the definitions, summary statistics of variables and hypothesized signs of variables
that are used in the analysis below.6 7

Insert Table 6:
Definitions, summary statistics, and hypothesized signs of variables

3.2.1. Time discounting biases

By comparing the mean values of the elicited discount rates, we examine whether our average respondent
displays the aforementioned two biases of time discounting. First, in any years, hyperbolic discounting or
the immediacy effect is not observed on average since the mean of discount rate DR₁, imputed from the5More detailed correlation coefficients by gender in each year are summarized in Appendix B.

6

Although the standardized average DISCRATE of the elicited discount rates should theoretically satisfy

E (DISCRATE) = 0 and σ (DISCRATE) = 1, neither equality is actually met as seen in the table. This comesfrom the fact that the numbers of effective responses differ among the five discount rate questions.
7

More detailed statistics by gender in each year are summarized in appendix A and C.

9immediate future choice (i.e., 2 days or 9 days), is not significantly higher than DR₂, applied to moredistant future choice (i.e., 90 days or 97 days). Secondly, in all years, the discount rate DR₄ applied for
future receipts is significantly higher than DR₅, the one used to discount future payments, which implies
that our average respondent displays the sign effect .8
To examine the effects of the time discounting biases on the respondents' cigarette consumption, weconstruct a binary indicator HYPERBOL for hyperbolic discounting and SIGN for the sign effect, where,e.g., HYPERBOL=1 if DR₁>DR₂ and HYPERBOL=0 otherwise. From the mean values of HYPERBOL
and SIGN, shown in Table 5, the proportions of the respondents who display the anomalies are 67.3% for
hyperbolic discounting and 90.3% for the sign effect. Our hypothesis is that, with other things being equal,
the respondents' cigarette consumption is positively related to HYPERBOL and negatively to SIGN.
In Table 5, the correlation coefficient of SIGN with cigarette consumption is significantly positive as
expected above. The coefficient of HYPERBOL is, however, negative, which is inconsistent with our
hypothesis. In section 4, we will discuss whether these results are robust after controlling other factors.

3.2.2. A proxy for procrastination and naïve hyperbolic discounting

To capture the immediacy effect, we also construct a proxy variable PROCR for hyperbolic discounting
and NAÏVE for naïve agent. To measure respondents' degrees of hyperbolic discounting or procrastination,
the JHPS survey asked them to indicate on a 5-point scale from 1 to 5 how they had been likely to
procrastinate doing homework assignments for the vacation in their childhood.9 Variable PROCR is the
response data to this question averaged over time, where a larger value implies a stronger tendency for8

In addition, although we have not put the results of the t test in the table, DR₃, the discount rate for JPY

10,000 (around USD 93.32), is significantly higher than DR₄, which applies for JPY 1 million (around USD
9331.84). This implies that people are more patient for larger amounts than for smaller amounts. This tendency
is called the magnitude effect (e.g., Benzion et al., 1989; Frederick et al., 2002).
9

In Japanese elementary and high schools, students are usually assigned many pieces of homework during

summer vacations.

10

procrastination or hyperbolic discounting. The JHPS survey also asked respondents in 2007 and 2008 how
they had projected doing homework assignments for the vacation in their childhood with the same point
scales as above. We define differences in PROCR from the response to this question and average themover time as NAÏVE, which indicates the degree of default in own planning. It is hypothesized that, withother things being equal, the respondents’ cigarette consumption depends positively on PROCR andNAÏVE. Table 5 shows that PROCR and NAÏVE have strongly positive correlation with smoking as
predicted.
Figure 2 shows the distributions of PROCR and NAÏVE. As shown in panel A, the proportion of people
who had done their homework assignment latterly, whose PROCR is more than 4, is slightly less than halfof the respondents. Meanwhile 67.1% of all respondents, shown in panel B, had been unable to do their
homework assignment when planned.

Insert Figure 2:
Distributions of PROCR and NAÏVE

3.3. Control variables

We define the following variables for controlling personal attributes: RISKAV, MALE, UNIV, INCOME,
AGE, and CHILD.
RISKAV measures the degree of risk aversion, which is constructed by subtracting from 100 the
respondents’ responses to the question: “When you go out, how high a probability of rainfall makes you
bring an umbrella with you?” We hypothesize that more risk-averse respondents are likely to consume lesscigarettes.
MALE is a binary indicator which equals 1 if the respondent is male and 0 if female. As shown in
Table 2 and Figure1, average cigarette consumption and smoking rates for males are larger than forfemales in our Japanese data, and therefore we naturally hypothesize that being male has positive effects
on smoking.
UNIV is a proxy variable of respondents’ education level, which equals 1 if the respondent had

11received college education and 0 otherwise. It was hypothesized that persons with a college backgroundsmoke less than others.
INCOME takes the value of per capita household income in ten thousand yen. Previous studies
empirically verify positive relation between income levels and the smoking status (e.g., Hersch, 2000; Ishii,2005).10 According to the literature, we hypothesize that persons earning higher incomes are likely to
consume less cigarettes.
AGE takes ages of respondents. Hersch (2000) shows that the effect of age on cigarette consumption
displays a nonlinear relation, which is significantly positive for the linear term and negative for thequadratic term. Ishii (2005) reports the same relation for japanese female samples. We hypothesize that theeffect of AGE on cigarette consumption is positive in the linear term and negative in the quadratic term.
CHILD is a binary indicator that takes 1 if the respondent has children and 0 otherwise. Hersch (2000)
reports that females having a youngest child under 2 years old decrease their cigarette consumption. Our
hypothesis is that females with children moderate their smoking behavior.
The correlation coefficients between control variables and SMOKING are shown in table 7.11

Insert Table 7:
Correlations between SMOKING and control variables

MALE displays a significantly positive coefficient and RISKAV and UNIV display negative
coefficients for all, male, and female samples as expected. The coefficient of INCOME for female samples
is significantly negative as hypothesized. On the other hand, for all and male samples, the coefficients of
INCOME are insignificant, which is consistent with Ishii (2005).
Coefficients of AGE are negative and strongly significant. These results are seemingly shown as

10

Hersch (2000) reports that high-income groups display lower smoking rates than other groups by using US

data. Ishii (2005) shows that, by using Japanese data, logarithmic household income displays a significantly
negative correlation with the amount of cigarette consumption for female samples and with smoking rates formale and female samples.
11

More detailed statistics by gender-year are summarized in Appendix D.

12contrary evidence for the literature and our hypothesis. As seen in Figure 3, however, which displays graphtrends of averaged cigarette consumption of smokers by gender in their 20s to 70s, cigarette consumption
looks like following quadratic trends. In the next section, we will use the linear and the quadratic terms of
AGE as explanatory variables for capturing the age trend of smoking.

Insert Figure 3:
Age trends of averaged cigarette consumption of smokers

As is pointed out in Hersch (2000), for female samples, CHILD displays a significantly negative
coefficient, which implies that the presence of children reduces cigarette consumption.12

4. Determinants of smoking behavior

In this section, we estimate the marginal effects on SMOKING using random-effects tobit models to
identify determinants of cigarette consumption. The following three models are estimated: model (1) uses
as explanatory variables DISCRATE, HYPERBOL, SIGN, and other control variables including TAX;
model (2) adopts PROCR, instead of HYPERBOL, to capture the effect of hyperbolic discounting; andmodel (3) adopts NAÏVE for hyperbolic discounting. All the models are estimated in all, male and female
samples. Table 8 shows the estimation results. All coefficients display the marginal effects which aremeasured around the mean values of smokers.

Insert Table8:
Random-effects tobit estimations for SMOKING

12

Unreported tests of the differences of the average of cigarette consumption between females with and withoutchildren including nonsmokers’ samples suggest that the former significantly smoke less than the latter, butusing only smokers’ samples suggest that having children is insignificant. This implies that having children
leads to quitting, rather than reducing, smoking.

13

4.1. Tax hikes

The coefficients of TAX are significantly negative for all and male samples, where hiking per cigarette tax
by one yen, ceteris paribus, decreases per capita cigarette consumption by 0.32 (model (3)) to 0.43 (model(2)) in all samples or by 0.49 (model (3)) to 0.60 (model (2)) in male samples. On the other hand, theinsignificant coefficients for the female sample suggest that there would be some female-specific factorsthat push up their smoking in our sample period. Differences in smoking moderation behavior under tax
hikes due to the types of time discounting and/or the degree of the biases will be estimated in the next
section.

4.2. Time discounting

In model (1), consistent with our hypothesis, DISCRATE displays positive effects on cigarette
consumption, which are, significant for all and female samples. The sign effect SIGN has expected
negative coefficients, which are significant for all and male samples. In contrast, the results regarding thecoefficients of HYPERBOL are weak. In particular,

they are insignificantly negative for all and malesamples.
In model (2) and model (3), all of DISCRATE, PROCR and NAÏVE display fairly significant
coefficients with the expected sign. Especially, all of them have strongly significant coefficients in the all
sample case. Male samples also display significant effects of these variables, and for female samples all
coefficients of time discounting variables except for the sign effect of model (2) are significant. All of them
are consistent with our expectations.
In sum, cigarette consumption has expected correlations with discount rates, procrastination, naiveness,and the sign effect. Discount rates, for example, that exceed the average by one unit of standard deviation,
ceteris paribus, increase the amount of cigarette consumption by 0.32 (model (2)) to 0.34 (model (3)) for
males and by 0.20 (model (3)) to 0.32 (model (1)) for females. The respondents who had procrastinatedabout doing their homework until the very end of the vacation (PROCR=5), ceteris paribus, smoke morethan respondents who had finished it at the beginning of the vacation (PROCR=1) by 1.096 for males and14

by 0.528 for females. The most naïve respondents (NAÏVE=4) smoke more than sophisticated ones
(NAÏVE=0) by 0.874 for males and by 0.539 for females. The respondents with the sign effect (SIGN=1)
consume less cigarettes than others, around 0.5 for males and 0.3-0.4 for females.

4.3. Control variables

Estimated coefficients of risk aversion variables (RISKAV) display expectedly negative signs , which arestrongly significant for all and female samples in all themodels. For example, cigarette consumption forfemale respondents who are risk averse such that their critical precipitation percentage to bring an umbrella
is 10% higher than the average is ceteris paribus lower than the average female by 0.13 (model (3)) to 0.14
(model (1) and (2)). The coefficients of MALE display significantly positive implying that male
respondents smoke more heavily than female ones by 5.21 (model (3)) to 5.71 (model (1)). Coefficients ofUNIV are strongly significant consistent with our expectations for all, male, and female in the entire
models. Respondents with a college background smoke less than others by 0.91 (model (3)) to 1.41 (model(2)) for males and by 1.43 (model (3)) to 1.48 (model (1)) for females. Smoking depend significantly on
AGE in a quadratic form in the entire models, such that cigarette consumption increase with aging atdecreasing rates until some critical age, and thereafter it decreases at increasing rates. The critical peaks ofsmoking are an age of 39.89 (i.e., 0.164/2*0.002) years (model (1)), 41.97 (model (2)) and 39.96(model(3)) for male smokers and 38.32 (model (1)), 38.37 (model (2)) and 40.29 (model (3)) for female smokers(after controlling other personal attributes). The estimated signs are consistent with our hypothesis, and thepeak ages for cigarette consumption are more or less the same by gender with other things being equal.
The coefficients of per capita household income (INCOME) are significantly negative and those of theincome squared (INCOME^2) are significantly positive only for female samples in the entire models. The
critical income levels of females at the bottom of cigarette consumption are 7.04 (model (1)), 7.04 (model(2)), and 7.03 million yen (model (3)). The coefficients of CHILD are, as are expected, significantly
negative for females suggesting that female respondents with children smoke less than others by 0.91
(model (3)) to 0.94 (model (1)).

155. The effects of tax hikes

In this section, we analyze the effects of time discounting biases on the reduction of cigarette consumption
responding to the hiking of cigarette tax. To do so, we sort the sample from the viewpoints of hyperbolic
discounting, the procrastinating tendency, and naïveness, and thereby compare the estimated coefficients of
TAX among the sorted samples. Our hypothesis is that cigarette consumption of hyperbolic discounting,procrastination, or naïve agents respond more negatively to tax hikes than other samples.

5.1. Responses to tax hikes with/without hyperbolic discounting

To detect the effect of hyperbolic discounting, respondents are classified by using HYPERBOL and PROC
as follows:(i)

Hyperbolic or exponential: Agents are classified as
hyperbolic if HYPERBOL= 1; andexponential if HYPERBOL= 0(ii)

The degree of procrastinating tendency is classified assevere procrastination if PROCR≧5;
modest procrastination if 5 > PROCR≧3; andweak/ no procrastination if PROCR< 3.

Table 9 shows the marginal effects of tax hikes on cigarette consumption for each type of agents byusing random-effects tobit models. All models use SMOKING for dependent variables, and are estimatedin all, male and female samples by controlling for DISCRATE, SIGN, RISKAV, MALE, UNIV, AGE,
AGE^2, INCOME, INCOME^2, and CHILD. The coefficients indicate the marginal effects of TAX.
Panel A of table 9 displays the estimated coefficients of TAX whose samples are sorted by using

16

HYPERBOL. For all and male samples, the coefficients of hyperbolic agents (HYPERBOL=1) aresignificantly negative and the coefficients of exponential agents (HYPERBOL=0) are insignificant. The
differences of the responses to tax hikes between hyperbolic and exponential agents are -0.186 in all
samples and -0.442 in the male samples, which are both negative as expected. These results imply that
hyperbolic agents reduced cigarette consumption after the tax hike more than exponential discounting
agents. For the female samples, the estimated coefficients are insignificant in both agents, however.
Panel B compares the effect of tax reduction on cigarette consumption among different degrees ofprocrastinating tendency. In the all sample case, the coefficients are significantly negative for respondents
with strong and modest procrastination while the coefficient is insignificantly negative for respondents
with weak/ no procrastination. The difference of the coefficients between the respondents with strong
procrastination and those with weak/ no procrastination is positive as expected. For males, the coefficientsfor respondents with strong procrastination and with weak/no procrastination are significantly negative,
where the difference between the coefficients is negative, consistent with our expectations. For the female
sample, although all the coefficients are insignificant, they have expected signs. In sum, hyperbolic or
procrastinative agents reduce their cigarette consumption more than exponential agents after tax hikes,
which implies that these agents postpone smoking moderation up to after-tax-hike period even though thetax hike was preannounced several months before..

Insert Table 9:Comparing the marginal effects of tax hikes for each agent（HYPERBOL and PROCR）

5.2. Naïve vs. SophisticatedWe also detect the effect of naiveness by defining the degree of naiveness as follows:(iii) The degree of naiveness: Respondents are classified as:
sophisticated if NAÏVE < 1;naïve if 1≦NAÏVE < 4; and17strongly naïve if NAÏVE = 4.

By sorting the sample by the degree of naiveness, Table 10 compares the marginal effects of tax hikes
on the cigarette consumption, which are estimated in the same manner as table 9. The values of theestimated coefficients and their differences are consistent with our expectations. In particular, thecoefficients for strongly naïve agents in all and female samples and those for naïve agents in all and malesamples are significant, where all of the differences exhibit negative signs as expected. These results imply
that naïve agents postpone smoking moderation even when a tax hike is pre-announced. It is only after theactual tax hike when they reduce cigarette consumption.

Insert Table 10:
Comparing the marginal effects of tax hikes for each agent（NAÏVE）

5.3. Policy implications

Cigarettes are addictive goods which have two important aspects related to policyl decisions: firstly,
habitual consumption of them damages health status, and consequently brings about decreases of social
welfare and increases of the medical care costs; and secondly the revenues from cigarette taxes are asignificant source of public finance. For the governments to decide the cigarette tax level by taking into
account both national health care and tax revenues, it is important to estimate the quantitative effects ofcigarette tax hikes on smoking behavior and on the resulting tax revenues.
Table 11 shows the estimates of cigarette consumption, the smoking rate, the smoking cessation rate,
and the tax revenue after tax hikes, where the three alternative tax hikes are considered: hikes by JPY 10,
20, and 35 (around USD 0.09, 0.19, and 0.33) per cigarette. All results are obtained from the tax elasticity
of cigarette demand for smokers, ranging from 0.079 for the heaviest smokers to 1.329 for the lightest
smokers.13
13

The elasticity are calculated from the marginal effects of TAX, -0.416, in model (1) for the all sample, whichis measured around the mean values of smokers in 2008. Appendices E and F show the cases of models (2) and18

In 2008, when most cigarettes are priced at JPY 300 (around USD 2.80) per pack, the average cigarette
consumption for smokers is 20.87, and the smoking rate is 25.1%. In the case of a tax hike by JPY 10(around USD 0.09) per cigarette, which is equivalent to setting the price at JPY 500 (i.e., 300+ (10*20))
per pack (around USD 4.67 per pack), the predicted average cigarette consumption after the tax hike is
18.9 and the smoking rate becomes 22.4%. Consequently, 10.7% of smokers reduce their cigarette
consumption in response to the tax hike, and the resulting increase in the rate of tax revenue is 73.2%. In
the case of a tax hike by JPY 20 (around USD 0.19) per cigarette, which corresponds to pricing at JPY 700(around USD 6.53) per pack, the smoking rate becomes 22.4%, and the resulting increase in the rate of tax
revenue is 107.2%. In the case of hiking tax by JPY 35 (around USD 0.33) per cigarette, which isequivalent to pricing at JPY 1000 (around USD 9.33) per pack, the smoking rate becomes 15.6%, and theresulting increase in the rate of tax revenue is 107.9%.
Figure 4 depicts the relationship between the amount of the per-cigarette tax hike and the associatedincreasing rate of the tax revenue. The government revenue reaches a peak when the per-cigarette tax hikeequals JPY 29.92 (around USD 0.28), with the corresponding per-pack cigarette price is JPY 898.4 (around
USD 8.38). The break-even tax-hike which keeps the tax revenues unchanged is estimated as JPY 58.85(around USD 0.55). This implies that even though the government sets the per-pack price of cigarettes at
JPY 1,477.09 (around USD 13.78), there is a room to raise tax revenues on one hand, and to decrease thedomestic smoking rate, on the other hand.14

Insert Table 11:
Tax hikes, smoking moderation, and tax revenue

model (3).
14On March 24th, the Japanese government has passed a bill on hiking the cigarette tax by JPY 3.5 (around

USD 0.03) per cigarette in October 1st, 2010. Following the passage, JT (Japan Tobacco, Inc.), the Japan’s
monopolistic cigarette manufacturer, has announced the decision to increase cigarette prices to around JPY 410
to 440 (around USD 3.83 to 4.11) per 20-cigarette pack. According to our estimation results, it is predicted that,by executing the tax- and price-increasing policy, smokers would decrease by 10.7%, and that government’s
revenue would increase by about 20.3 to 28.4%.

19

Insert Figure 4:
Tax hikes and the increasing rates of tax revenue

6. Conclusion
By using our broad panel survey of Japanese adults, we have found that discount rates and its behavioral
biases affect smoking behavior. Respondents’ discount rates are higher, or their procrastinating tendency or
naiveness is stronger, they are likely to smoke more than others. In the response to cigarette tax hikes,
hyperbolic, procrastinating or naïve agents postpone smoking moderation up to after-tax-hike period eventhough the tax hike was preannounced several months before.
We have also clarified that the Japanese government is able to hike cigarette taxes for increasing tax
revenue. The government’s tax-rising policy, however, does not necessary lead to consumer’s welfare
improvement. It causes welfare losses to exponential discounters, whereas it works as a commitment
devise for hyperbolic discounters and consequently increases their welfare level. As the further research,
we would like to incorporate heterogeneity of discounting function to determine precise welfare-changing
processes and the quantities induced by tax-changing policies.

20References

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23

Table 1: The number of samples and frequencies of responses

Num. of samples

Freq. of responses

2005
2,987

2006
3,763

Four times Three times
1,824
1,125

2007
3,112

2008
4,018

Total
13,880

Twice
488

Once
2,233

Total
5,670

Note: The data are from the Japan Household Panel Survey on Consumer Preferences and Satisfaction
from 2005 to 2008. The row of “Num. of samples” shows the number of respondents each year. The row of“Freq. of respondents” shows the number of frequencies in responses during our sample period.

24

Table2: S ummary statistics of smoking behavior(Panel A) Cigarette consumption

M ean
S.D.
Freq.
M ean
M ale S.D.
Freq.
M ean
Female S.D.
Freq.
All

2005
6.502
11.971
2,972
11.37314.728
1,395
2.193
6.223
1,577

2006
6.226
11.6793,746
10.93714.378
1,763
2.038
6.054
1,983

2007
5.873
11.294
3,084
10.21813.703
1,437
2.081
6.649
1,647

2008
5.238
10.7244,001
9.172
13.394
1,870
1.785
5.748
2,131

2006
21.87911.6971,066
24.253
11.635795
14.916
8.734
271

2007
21.82011.222830
23.76010.712618
16.16610.777212

2008
20.873
11.4881,004
23.368
11.193734
14.092
9.355
270

2006
0.258
0.437
3,746
0.419
0.494
1,763
0.114
0.318
1,983

2007
0.245
0.430
3,084
0.404
0.491
1,437
0.106
0.308
1,647

2008
0.224
0.417
4,001
0.367
0.482
1,870
0.099
0.299
2,131

(Panel B) Cigarette consumption (S mokers)

M ean
S.D.
Freq.
M ean
M ale S.D.
Freq.
M ean
Female S.D.
Freq.
All

2005
21.51712.296898
24.48312.077648
13.8329.136
250(Panel C) S moking rates

M ean
S.D.
Freq.
M ean
M ale S.D.
Freq.
M ean
Female S.D.
Freq.
All

2005
0.264
0.441
2,972
0.424
0.494
1,395
0.122
0.327
1,577

Note: The data are from the Japan Household Panel Survey on Consumer Preferences and Satisfaction
from 2005 to 2008. Summary statistics of Panel A include nonsmokers in samples. Panel B shows thestatistics only smokers. Panel C shows rates of regular smokers.

25

Table 3: Question to elicit discount rates: An example (QUESTION 1 for DR1)
Suppose you have two options to receive some money. You may choose Option “A”, to receive
10,000 JPY in two days; or Option “B”, to receive a different amount in nine days. Compare theamounts and timing in Option “A” with Option “B” and indicate which amount you would prefer toreceive for each of all 8 choices.

Option A

Option B

Interest rate

(Receipt in 2 days)(Receipt in 9 days)(Annual)JPY 10,000 (USD 93.32)

JPY 9,981 (USD 93.14)

-10%

A

B

JPY 10,000 (USD 93.32)

JPY 10,000 (USD 93.32)

0%

A

B

JPY 10,000 (USD 93.32)

JPY 10,019 (USD 93.50)

10%

A

B

JPY 10,000 (USD 93.32)

JPY 10,038 (USD 93.67)

20%

A

B

JPY 10,000 (USD 93.32)

JPY 10,096 (USD 94.21)

50%

A

B

JPY 10,000 (USD 93.32)

JPY 10,191 (USD 95.10)

100%

A

B

JPY 10,000 (USD 93.32)

JPY 10,383 (USD 96.89)

200%

A

B

JPY 10,000 (USD 93.32)

JPY 10,574 (USD 98.67)

300%

A

B

Circle A or B.

Note: This is a question in the Japan Household Panel Survey on Consumer Preferences and Satisfaction
from 2005 to 2008. The US dollar amounts are computed by using the average JPY/ USD exchange rate,
107.16, in February, 2008.

26

27

receipt

JPY 10,000(USD 93.32)

90 days or 97 days

DR2

receipt

JPY 10,000(USD 93.32)

1 month or 13 months

DR3

receipt

JPY 1million(USD 9331.84)

1 month or 13 months

DR4

payment

JPY 1million(USD 9331.84)

1 month or 13 months

DR5

sign effect:

DR4>DR5

Note: This is a question in the Japan Household Panel Survey on Consumer Preferences and Satisfaction from 2005 to 2008. The US dollar amounts are computed byusing the average JPY/ USD exchange rate, 107.16, in February, 2008.

hyperbolic discounting: DR1>DR2

Mean S.D.
Obs.
Mean S.D.
Obs.
Mean S.D.
Obs.
Mean S.D.
Obs.
Mean S.D.
Obs.
1.904 2.390 2,737 1.892 2.421 2,768 0.153 0.193 2,790 0.023 0.042 2,771 -0.008 0.044 2,331
1.798 2.274 3,444 1.843 2.387 3,464 0.147 0.183 3,475 0.024 0.042 3,475 -0.008 0.040 2,912
1.591 1.968 2,905 1.623 2.059 2,872 0.137 0.166 2,902 0.022 0.036 2,891 -0.001 0.028 2,292
1.561 1.952 3,761 1.515 1.941 3,765 0.136 0.166 3,765 0.022 0.036 3,756 -0.002 0.027 3,103
1.704 2.148 12,847 1.708 2.206 12,869 0.143 0.177 12,932 0.023 0.039 12,893 -0.005 0.036 10,638

receipt

Receipt or
payment

2005
2006
2007
2008
PooledJPY 10,000(USD 93.32)

2 days or 9 days

Amounts for (A)Time discounting biases

Choiceconditions

Timings((A) or (B))

DR1

Table 4: Elicited discount rates under alternative choice conditions

Table 5: Correlation coefficients of time discounting variables with S MOKINGDISCRATE
HYPERBOL
SIGN
PROCR
NAÏVE

All
0.116 ***
(0.000)-0.048 ***(0.000)-0.024 **(0.014)0.124 ***
(0.000)0.063 ***
(0.000)SM OKING
M ale
0.075 ***
(0.000)-0.039 ***(0.003)-0.035 **(0.015)0.084 ***
(0.000)0.063 ***
(0.000)Female0.045 ***
(0.001)-0.015(0.219)-0.024 *(0.078)0.059 ***
(0.000)0.057 ***
(0.000)Note: This table shows the correlation coefficients of cigarette consumption
with time discounting variables. Samples include nonsmokers.
P-values are in parentheses.
*, **, *** denote statistical significance at the 10%, 5%, and 10% levels, respectively.

28

Table 6: Definition, summary statistics, and hypothesized signs of variables
Variables

Definition

Mean

S.D.

Obs.

The amount of cigarette consumptioon per day.

5.920

11.39813,803

Taxation level per cigarette. 7.892 yen from 2005 to 2006 and 8.744 yenfrom 2007 to 2008.

8.330

0.426

13,880-

Simple mean, defined by equation (1), of the standardized values of the
DISCRATE elicited discount rates DRi (i = 1, … ,5) as a measure of the degree of
impatience.

0.042

0.718

10,125+

HYPERBOL

A binary indicator for hyperbolic discounting which equals 1 if DR1 > DR2 ,and 0 otherwise.

0.673

0.469

12,639+PROCRTime averaged response to the question ‘When did you do homework for thevacation in your childhood? ’ on a 5-poit scale, from 1 (Homework was
finished at ‘the beginning of the vacation’ ) to 5 (it was not done until ‘thevery end of the vacation ’ ), which is a proxy measure of the degree ofprocrastination.

3.262

1.184

13,827+

NAÏVETime averaged difference between respondents' plan and execution to do
homework in summer vacation in their high school day.

1.117

1.206

11,638+0.296

10,450-20.203

13,654-

Cigaretteconsumption

SMOKING

Taxation
level

TAX

Time
Preferences
factors

SIGN

RISKAV

Controlvariables

A binary indicator for the sign effect which equals 1 if DR4 > DR5 , and 0
0.903
otherwise.
A variable which measures the degree of risk aversion, constructed bysubtracting from 100 the respondents' responses to the question: "When you
go out, how high probability of rainfall makes you bring an umbrella with 50.160
you?"

Hypothesis

MALE

A binary indicator for males which equals 1 for male respondents and 0
otherwise.

0.469

0.499

13,880+UNIV

A binary indicator for university graduates which equals 1 for university
graduates and 0 otherwise.

0.218

0.413

13,454-223.615174.910

11,60849.68613.05813,880

+

0.811

0.391

13,639-INCOMEAGE
CHILD

Per capita household income in ten thousand yen.
Ages of respondents.
A binary indicator for parent of children which equals 1 for respondents withchildlen and 0 otherwise.

Note: "Hypothesis" shows the theoritically predicted signs of associations with smoking.

29

Table 7: Correlations between control variables and S MOKING

M ALE
UNIV
RISKAVINCOM EAGE
CHILD

SM OKINGAll
M ale
Female0.366 *** NANA(0.000)-0.017 **-0.130 ***-0.067 ***(0.050)(0.000)(0.000)-0.099 ***-0.111 ***-0.062 ***(0.000)(0.000)(0.000)0.003
0.001-0.042 ***(0.719)(0.927)(0.001)-0.034 ***-0.056 ***-0.107 ***(0.000)(0.000)(0.000)-0.032 ***0.003-0.039 ***(0.000)(0.820)(0.001)

Notes: This table shows the correlation coefficients of cigarette consumption with
control variables. Samples include nonsmokers.
P-values are in parentheses.
*, **, *** denote statistical significance at the 10%, 5%, and 10% levels, respectively.

30

31

-0.0070.164 **

4277
-8479.015

2112

4086

-3625.779

2165

4297

(0.000)-0.942 ***(0.018)0.000 **

(0.002)-0.003 ***(0.000)-0.003 ***(0.000)0.238 ***

(0.000)-1.475 ***(0.001)-0.014 ***(0.168)-0.318(0.141)

0.248(0.007)0.323 ***

(0.431)-0.125

Coef.
(P-value)

Female-12130.185

4253

8359

(0.234)-0.337(0.099)0.000 *

(0.150)-0.001(0.000)-0.004 ***(0.000)0.354 ***

(0.000)-2.027 ***(0.000)5.666 ***

(0.000)-0.016 ***(0.003)-0.687 ***(0.000)0.355 ***

(0.000)0.373 ***

(0.002)-0.430 ***Coef.
(P-value)All-8429.206

2100

4074

(0.123)0.494(0.984)0.000(0.676)0.000(0.001)-0.003 ***(0.006)0.211 ***

(0.000)-1.405 ***(0.136)-0.007(0.092)-0.481 *(0.016)

0.274 **

(0.013)0.316 **

(0.001)-0.597 ***Coef.
(P-value)

M ale

Model (2)-3621.708

2153

4285

(0.000)-0.926 ***(0.020)0.000 **

(0.003)-0.003 ***(0.000)-0.003 ***(0.000)0.233 ***

(0.000)-1.467 ***(0.001)-0.014 ***(0.157)-0.328(0.045)0.132 **

(0.025)0.245 **

(0.581)-0.087

Coef.
(P-value)

Female-9846.033

3355

7181

(0.978)-0.007(0.126)0.000(0.133)-0.001(0.000)-0.004 ***(0.000)0.280 ***

(0.000)-1.528 ***(0.000)5.210 ***

(0.001)-0.014 ***(0.006)-0.658 ***(0.002)

0.237 ***

(0.001)0.353 ***

(0.024)-0.318 **Coef.
(P-value)All-6684.307

1575

3372

(0.148)0.514(0.848)0.000(0.520)0.001(0.020)-0.002 **(0.067)0.160 *

(0.001)-0.907 ***(0.097)-0.008 *(0.102)-0.512(0.049)0.219 **

(0.013)0.338 **

(0.009)-0.490 ***Coef.
(P-value)

M ale

M odel (3)Female-3091.643

1780

3809

(0.001)-0.914 ***(0.008)0.000 ***

(0.001)-0.004 ***(0.000)-0.004 ***(0.000)0.342 ***

(0.000)-1.428 ***(0.002)-0.013 ***(0.066)-0.467 *(0.057)0.135 *

(0.085)0.204 *

(0.807)-0.041

Coef.
(P-value)*, **, *** denote statistical significance at the 10%, 5%, and 10% levels, respectively.

P-values are in parentheses.

Notes: This table shows the results of random-effects tobit model. Estimated coefficients are transformed into the marginal effects for the expected cigarette consumption conditional on
SMOKING>0.

-12184.501

8383

0.424
(0.252)-0.416(0.146)0.000(0.867)0.000(0.115)0.001(0.618)-0.001(0.118)-0.002 ***(0.008)-0.004 ***(0.041)(0.000)(0.000)0.339 ***

-1.339 ***(0.000)-2.057 ***(0.114)(0.000)(0.000)5.714 ***

(0.000)-0.016 ***-0.495 *(0.083)-0.631 ***(0.007)-0.288(0.131)-0.029Num. of GroupLog likelihood

0.180(0.192)(0.849)(0.000)0.389 ***

-0.536 ***(0.003)-0.422 ***(0.003)Coef.
(P-value)Coef.
(P-value)

Num. of Obs.

CHILD

INCOM E^2

INCOM E

AGE^2

AGE

UNIV

M ALE

RISKAV

SIGN

NAÏVE

PROCR

HYPERBOLDISCRATETAXSM OKING

M ale

All

M odel (1)Table 8. Random-effects tobit estimations for SMOKING

Table 9: Comparing the marginal effects of tax hikes for each agent （ HYPERBOL and PROCR）(Panel A) HYPERBOL

All
Difference
M ale
Difference
FemaleDifference

Hyperbolic
Exponential(HYPERBOL=1) (HYPERBOL=0)-0.412 **-0.226(0.023)(0.415)-0.186-0.642 ***-0.199(0.007)(0.497)0.442-0.154-0.181(0.452)(0.634)0.027(Panel B) PROCR
Strongprocrastination(PROCR=5)
All
Difference
M ale
Difference
FemaleDifference-0.975 **(0.047)-0.524-1.120 *(0.061)-0.153-0.643(0.403)-0.802

Procrastination(5>PROCR≧3)-0.393 **(0.043)

Weak/ No
procrastination(PROCR<3)-0.451(0.174)-0.336(0.139)-0.968 ***(0.002)-0.487(0.402)0.159(0.525)

Notes: This tables show the coefficients of marginal effects of TAX by estimating randomeffects tobit models. Samples are sorted by using NAIVE. SM OKING is used as an explained
variable and DRSTD, SIGN, RISKAV, MALE, UNIV, AGE, AGE^2, INCOM E,
INCOME^2, and CHILD are used as explanatory variables.
P-values are in parentheses.
*, **, *** denote statistical significance at the 10%, 5%, and 10% levels, respectively .
"Difference" shows the differences between marginal effects of Hyp erbolic and Exponential in
Panel A, and the differences between marginal effeccts of Strong procrastination and Weak/ No
Procrastination in Panel B.

32

Table 10: Comparing the marginal effects of tax hikes for each agent （ NAÏVE）

All
Difference
M ale
Difference
FemaleDifference

Strong naïve
(NAÏVE=4)-2.106 **(0.012)-1.966-1.512(0.180)-1.105-3.907 ***(0.001)-4.100Naïve(4>NAÏVE≧1)-0.344 ***(0.006)

Sophisticate(NAÏVE<1)-0.140(0.551)-1.296 **(0.030)-0.407(0.202)-2.678(0.623)0.193(0.507)

Notes: This table shows the coefficients of marginal effects of TAX by estimating randomeffects tobit models. Samples are sorted by using NAIVE. SM OKING is used as an explained
variable and DRSTD, SIGN, RISKAV, M ALE, UNIV, AGE, AGE^2, INCOM E,
INCOM E^2, and CHILD are used as explanatory variables.
P-values are in parentheses.
*, **, *** denote statistical significance at the 10%, 5%, and 10% levels, respectively.
"Difference" shows the differences between marginal effects of Strong naive and Sophisticate.

33

Table 11: Tax hikes, smoking moderation, and increasing rates of tax revenue
Model (1): The marginal effect of TAX= -0.416
before tax hikes
JPY 300
Cigarette price per pack
(USD 2.80)
JPY 0⊿TAX per cigarette(USD 0)
Averaged cigarette consumption
20.873
Smoking rates
25.1%
Smoking cessation rates for smokers
0.0%
Increasing rates of revenues
0.0%

JPY 500(USD 4.67)
JPY 10(USD 0.09)
18.880
22.4%
10.7%
73.2%

after tax hikes
JPY 700(USD 6.53)
JPY 20(USD 0.19)
14.72522.4%
10.7%
107.2%

JPY 1000(USD 9.33)
JPY 35(USD 0.33)
13.95615.6%
37.8%
107.9%

Notes: This tabel shows the predicted smoking status and increasing rates of government's revenues responding to tax
hikes by JPY 10, 20, and 35 (around USD 0.09, 0.19, and 0.33) per cigarette. The estimated coefficient of TAX inmodel (1) for full samples is used for the predictions. The marginal effect is measured around mean values of smokers
in 2008. The row of "Cigarette price per pack" shows the hypothetical prices per 20-cigarette pack after tax hikes
calculated by (20*⊿TAX per cigarette). The row of "⊿TAX per cigarette" shows the increased rates of tax per
cigarette. The rows of "Averaged cigarette consumption", "Smoking rates", and "Smoking cessation rates for smokers"
show the resulting status of cigarette consumption after tax hikes. The row of "Increasing rates of revenues" shows thenet increasing rates of government's revenues by hiking cigarette tax.

34

Figure 1: Transition graphs of smoking behavior(Panel A) Cigarette consumption
12.0
10.0
8.0
6.0
4.0
2.0
0.0
2005

2006

2007

2008

(Panel B) Cigarette consumption (Smokers)
24.0
22.0
20.0
18.0
16.0
14.0
12.0
2005

2006

2007

2008

2006

2007

2008

(Panel C) Smoking rates
45%
40%
35%
30%
25%
20%
15%
10%
5%
2005
All

M alesFemales

Note: The graphs show trends of smoking behavior. Panel A shows averaged cigarette
consumption including nonsmokers in samples. Panel B shows ageraged cigarette
consumption among smokers. Panel C shows rates of regular smokers.

35

Figure 2: Distributions of PROCR and NAÏVE
(Panel A) PROCR
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
1〜2

2〜3

3〜4

4〜5

5

(Panel B) NAÏVE
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
-2

-2〜-1

-1〜0

0〜1

1〜2

36

2〜3

3〜4

Figure 3: Age trends of averaged cigarette consumption of smokers(Pane l A) All
8
7
6
5
4
3
20's

30's

40's

50's

60's

70's

30's

40's

50's

60's

70's

40's

50's

60's

70's

(Panel B) Males
16
14
12
10
8
6
4
20's

(Panel C) Fe male s
4
3.5
3
2.5
2
1.5
1
0.5
0
20's

30's
2005

2006

2007

2008

PooledNotes: the graphs show the age trends of averaged cigarette consumption including
nonsmokers. The horizonal axes indicate the averaged number of cigarette consumption.
The vertical axes indicate age brackets.

37

Figure 4: Tax hikes and the increasing rates of tax revenue
The marginal effect of TAX= -0.416 (model (1))

(Unit: JPY)
increasing rates of tax revenue

200%
150%
100%
50%
0%
-50% 0

20

40

60

80

100

120

140

160

-100%
⊿TAX
Notes: This figure shows the predicted increasing rates of government's revenues
responding to tax hikes. The horizonal axis indicates increased rates of tax per
cigarette. The vertical axis indicates net increasing rates of government's revenues
by hiking cigarette tax in JPY terms. The estimated coefficient of TAX in model (1)for full samples is used for the predictions. The marginal effect is measured aroundmean values of smokers in 2008.

38

Appendix A: Summary statistics of time preferences
All samples

Male samples

Female samples

2005

2005

2005

Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Obs
2737
2768
2790
2771
2331
2202
2694
2289
2963
1951

M ean Std. Dev.
1.904
2.390
1.892
2.421
0.153
0.193
0.023
0.042
-0.008
0.044
0.042
0.688
0.621
0.485
0.885
0.319
3.263
1.195
1.087
1.196

M in
-0.369-0.373-0.121-0.065-0.073-1.376
0
0
1
-2

M ax
5.724
5.742
0.512
0.118
0.117
2.033
1
1
5
4

2006
Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Obs
3444
3464
3475
3475
2912
2784
3412
2862
3747
2962

M ean Std. Dev.
1.798
2.274
1.843
2.387
0.147
0.183
0.024
0.042
-0.008
0.040
0.033
0.693
0.585
0.493
0.902
0.297
3.277
1.171
1.120
1.201

M in
-0.363-0.372-0.117-0.065-0.070-1.396
0
0
1
-2

M ax
5.537
5.675
0.503
0.118
0.115
2.099
1
1
5
4

Obs
2905
2872
2902
2891
2292
2190
2844
2249
3112
2962

M ean Std. Dev.
1.591
1.968
1.623
2.059
0.137
0.166
0.022
0.036
-0.001
0.028
0.048
0.753
0.587
0.492
0.914
0.280
3.260
1.162
1.120
1.201

M in
-0.339-0.348-0.110-0.062-0.061-1.580
0
0
1
-2

M ax
5.074
5.188
0.487
0.114
0.108
2.407
1
1
5
4

M in
-0.369-0.373-0.121-0.065-0.073-1.376
0
0
1
-2

M ax
5.724
5.742
0.512
0.118
0.117
2.033
1
1
5
4

Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Obs
3761
3765
3765
3756
3103
2949
3689
3050
4005
3763

M ean Std. Dev.
1.561
1.952
1.515
1.941
0.136
0.166
0.022
0.036
-0.002
0.027
0.045
0.737
0.860
0.347
0.910
0.287
3.248
1.205
1.129
1.219

M in
-0.339-0.341-0.110-0.062-0.061-1.576
0
0
1
-2

M ax
5.041
5.017
0.487
0.114
0.108
2.450
1
1
5
4

Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Obs
1631
1639
1655
1656
1386
1322
1611
1363
1767
1348

M ean Std. Dev.
2.060
2.361
2.111
2.489
0.170
0.191
0.031
0.044
-0.005
0.042
0.158
0.730
0.540
0.499
0.902
0.298
3.487
1.135
1.165
1.204

M in
-0.363-0.372-0.117-0.065-0.070-1.396
0
0
1
-2

M ax
5.537
5.675
0.503
0.118
0.115
2.099
1
1
5
4

Obs
1350
1331
1350
1345
1071
1010
1317
1046
1449
1348

M ean Std. Dev.
1.851
2.059
1.847
2.136
0.162
0.176
0.029
0.038
0.002
0.031
0.196
0.795
0.547
0.498
0.922
0.269
3.467
1.124
1.165
1.204

M in
-0.339-0.348-0.110-0.062-0.061-1.580
0
0
1
-2

M ax
5.074
5.188
0.487
0.114
0.108
2.407
1
1
5
4

M ean Std. Dev.
1.704
2.148
1.708
2.206
0.143
0.177
0.023
0.039
-0.005
0.036
0.042
0.718
0.673
0.469
0.903
0.296
3.262
1.184
1.111
1.202

M in
-0.369-0.373-0.121-0.065-0.073-1.580
0
0
1
-2

M ax
5.724
5.742
0.512
0.118
0.117
2.450
1
1
5
4

Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Obs
1441
1461
1470
1459
1215
1151
1423
1193
1568
1091

M ean Std. Dev.
1.739
2.332
1.728
2.351
0.136
0.186
0.018
0.040
-0.0100.043-0.040
0.660
0.644
0.479
0.884
0.320
3.066
1.196
1.055
1.179

M in
-0.369-0.373-0.121-0.065-0.073-1.376
0
0
1
-2

M ax
5.724
5.742
0.512
0.118
0.117
2.033
1
1
5
4

Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Obs
1813
1825
1820
1819
1526
1462
1801
1499
1980
1614

M ean Std. Dev.
1.563
2.168
1.602
2.265
0.125
0.173
0.017
0.039
-0.0100.038-0.079
0.637
0.625
0.484
0.903
0.297
3.090
1.171
1.083
1.198

M in
-0.363-0.372-0.117-0.065-0.070-1.396
0
0
1
-2

M ax
5.537
5.675
0.503
0.118
0.115
2.099
1
1
5
4

Obs
1555
1541
1552
1546
1221
1180
1527
1203
1663
1614

M ean Std. Dev.
1.365
1.857
1.430
1.971
0.116
0.155
0.016
0.032
-0.0040.025-0.079
0.691
0.622
0.485
0.908
0.290
3.079
1.165
1.083
1.198

M in
-0.339-0.348-0.110-0.062-0.061-1.580
0
0
1
-2

M ax
5.074
5.188
0.487
0.114
0.108
2.407
1
1
5
4

Obs
2000
2003
2005
1999
1622
1533
1960
1594
2133
2052

M ean Std. Dev.
1.334
1.842
1.307
1.822
0.117
0.155
0.018
0.033
-0.0050.024-0.074
0.674
0.864
0.343
0.907
0.291
3.072
1.208
1.112
1.228

M in
-0.339-0.341-0.110-0.062-0.061-1.576
0
0
1
-2

M ax
5.041
5.017
0.487
0.114
0.108
2.450
1
1
5
4

Obs
6809
6830
6847
6823
5584
5326
6711
5489
7344
7779

M ean Std. Dev.
1.488
2.052
1.503
2.104
0.123
0.167
0.017
0.036
-0.0070.033-0.069
0.665
0.698
0.459
0.901
0.299
3.077
1.186
1.080
1.197

M in
-0.369-0.373-0.121-0.065-0.073-1.580
0
0
1
-2

M ax
5.724
5.742
0.512
0.118
0.117
2.450
1
1
5
4

2007
Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE
2008
Obs
1761
1762
1760
1757
1481
1416
1729
1456
1872
1711

M ean Std. Dev.
1.819
2.040
1.752
2.043
0.157
0.176
0.028
0.039
0.000
0.030
0.173
0.781
0.855
0.352
0.913
0.282
3.450
1.171
1.148
1.208

M in
-0.339-0.341-0.110-0.062-0.061-1.576
0
0
1
-2

M ax
5.041
5.017
0.487
0.114
0.108
2.450
1
1
5
4

Pooled
Obs
12847
12869
12932
12893
10638
10125
12639
10450
13827
14156

Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE
2006

2008

PooledVariable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

M ean Std. Dev.
2.088
2.442
2.075
2.484
0.172
0.198
0.028
0.043
-0.006
0.045
0.132
0.707
0.595
0.491
0.885
0.319
3.485
1.155
1.129
1.217

2007

2008
Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Obs
1296
1307
1320
1312
1116
1051
1271
1096
1395
860

2006

2007
Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE
Pooled

Obs
6038
6039
6085
6070
5054
4799
5928
4961
6483
6377

M ean Std. Dev.
1.949
2.227
1.940
2.294
0.165
0.185
0.029
0.041
-0.002
0.038
0.165
0.754
0.645
0.478
0.905
0.293
3.471
1.147
1.149
1.208

39

M in
-0.369-0.373-0.121-0.065-0.073-1.580
0
0
1
-2

M ax
5.724
5.742
0.512
0.118
0.117
2.450
1
1
5
4

Variable
DR1
DR2
DR3
DR4
DR5
DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Appendix B: Correlation coefficients of time discounting variables with S MOKING2005

DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

2006

All
0.094 ***
(0.000)-0.044 **(0.024)-0.036 *(0.090)0.140 ***
(0.000)0.087 ***
(0.000)SM OKING
M ale
0.053 *
(0.087)-0.020(0.474)-0.043(0.156)0.080 ***
(0.003)0.080 **
(0.019)Female0.056 *
(0.057)-0.047 *(0.078)-0.040(0.171)0.100 ***
(0.000)0.101 ***
(0.001)2007

DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

All
0.127 ***
(0.000)-0.069 ***(0.000)-0.025(0.189)0.135 ***
(0.000)0.062 ***
(0.001)SMOKING
Male
0.091 ***
(0.001)-0.055 **(0.027)-0.033(0.228)0.096 ***
(0.000)0.057 **
(0.037)

Female0.029(0.271)-0.013(0.591)-0.017(0.515)0.064 ***
(0.005)0.060 **
(0.015)All
0.105 ***
(0.000)0.042 **
(0.012)0.004(0.842)
0.095 ***
(0.000)0.042 **
(0.010)SMOKINGMale
0.062 **
(0.021)
0.052 **
(0.033)0.003(0.914)0.059 **
(0.011)
0.048 **
(0.046)

Female0.040(0.120)0.056 **
(0.014)-0.006(0.799)0.027(0.221)
0.031(0.166)2008

All
0.140 ***
(0.000)-0.074 ***(0.000)-0.040 *(0.062)0.132 ***
(0.000)0.069 ***
(0.000)SM OKING
M ale
0.100 ***
(0.002)-0.065 **(0.018)-0.070 **(0.025)0.103 ***
(0.000)0.073 ***
(0.008)

Female0.056 *
(0.056)-0.028(0.282)-0.035(0.233)0.053 **
(0.033)0.055 **
(0.029)All
0.116 ***
(0.000)-0.048 ***(0.000)-0.024 **(0.014)0.124 ***
(0.000)0.063 ***
(0.000)SM OKING
M ale
0.075 ***
(0.000)-0.039 ***(0.003)-0.035 **(0.015)0.084 ***
(0.000)0.063 ***
(0.000)Female0.045 ***
(0.001)-0.015(0.219)-0.024 *(0.078)0.059 ***
(0.000)0.057 ***
(0.000)DISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

PooledDISCRATE
HYPERBOL
SIGN
PROCR
NAIVE

Notes: The tables show the correlation coefficients of cigarette consumption with time discounting variables. Samples include nonsmokers.
P-values are in parentheses.
*, **, *** denote statistical significance at the 10%, 5%, and 10% levels, respectively.

40

Appendix C(a): Summary statistics of SMOKING and control variables (All samples)
2005
Variable
SM OKING
SM OKING>0
M ALE
UNIV
RISKAVINCOM E
AGE
CHILD

Obs
2972
898
2987
2893
2941
2361
2987
2939

M ean
6.502
21.5170.470
0.204
50.478223.208
49.0770.806

Std. Dev.
11.971
12.2960.499
0.403
20.489164.655
12.962
0.395

M in

Obs
3746
1066
3763
3632
3690
3085
3763
3679

M ean
6.226
21.8790.471
0.211
50.063220.669
49.2290.809

Std. Dev.
11.67911.6970.499
0.408
20.196176.19613.111
0.393

M in

Obs
3084
830
3112
3029
3063
2633
3112
3069

M ean
5.873
21.8200.466
0.212
50.250224.45350.441
0.824

Std. Dev.
11.29411.2220.499
0.409
20.057179.273
12.849
0.381

M in

Obs
4001
1004
4018
3900
3960
3529
4018
3952

M ean
5.238
20.8730.468
0.239
49.944225.83749.9810.808

Std. Dev.
10.72411.4880.499
0.427
20.112
177.167
13.205
0.394

M in

Obs
13803
3798
13880
13454
13654
11608
13880
13639

M ean
5.920
21.5150.469
0.218
50.160223.61549.6860.811

Std. Dev.
11.39811.6880.499
0.413
20.203174.91013.058
0.391

M in

0
2.739
0
0
0
11.667210

M ax
46.62246.622
1
1
100
1500
71
1

2006
Variable
SM OKING
SM OKING>0
M ALE
UNIV
RISKAVINCOM E
AGE
CHILD

0
2.767
0
0
0
11.667190

M ax
46.28646.286
1
1
100
1700
72
1

2007
Variable
SM OKING
SM OKING>0
M ALE
UNIV
RISKAVINCOM E
AGE
CHILD

0
2.710
0
0
0
17.50020
0M ax
45.61945.619
1
1
100
3000
73
1

2008
Variable
SM OKING
SM OKING>0
M ALE
UNIV
RISKAVINCOM E
AGE
CHILD

0
2.734
0
0
0
7.000
20
0

M ax
45.82545.825
1
1
100
1700
74
1

PooledVariable
SM OKING
SM OKING>0
M ALE
UNIV
RISKAVINCOM E
AGE
CHILD

0
2.710
0
0
0
7.000
19
0

41

M ax
46.62246.622
1
1
100
3000
74
1

Appendix C(b): Summary statistics of SMOKING and control variables (male samples)
2005
Variable
SM OKING
SM OKING>0UNIV
RISKAVINCOM E
AGE
CHILD

Obs
1395
648
1364
1385
1141
1403
1375

M ean
11.37324.4830.297
49.558231.02250.1590.774

Std. Dev.
14.72812.0770.457
21.259173.059
12.875
0.419

M in

Obs
1763
795
1715
1734
1500
1774
1729

M ean
10.93724.2530.307
49.156231.259
50.231
0.782

Std. Dev.
14.378
11.6350.461
21.141
187.84413.045
0.413

M in

Obs
1437
618
1412
1426
1266
1449
1421

M ean
10.21823.7600.311
49.471232.34251.6020.806

Std. Dev.
13.70310.7120.463
20.720190.234
12.756
0.395

M in

Obs
1870
734
1825
1849
1700
1879
1838

M ean
9.172
23.3680.351
49.063229.309
51.0900.782

Std. Dev.
13.39411.1930.477
20.983176.04213.093
0.413

M in

Obs
6465
2795
6316
6394
5607
6505
6363

M ean
10.36123.9650.318
49.286230.864
50.7690.786

Std. Dev.
14.051
11.4300.466
21.024181.89312.969
0.410

M in

0
2.753
0
0
14
21
0

M ax
46.62246.622
1
100
1500
71
1

2006
Variable
SM OKING
SM OKING>0UNIV
RISKAVINCOM E
AGE
CHILD

0
2.767
0
0
11.667190

M ax
46.28646.286
1
100
1700
72
1

2007
Variable
SM OKING
SM OKING>0UNIV
RISKAVINCOM E
AGE
CHILD

0
2.797
0
0
17.50021
0M ax
45.61945.619
1
100
3000
73
1

2008
Variable
SM OKING
SM OKING>0UNIV
RISKAVINCOM E
AGE
CHILD

0
2.774
0
0
7.000
20
0

M ax
45.82545.825
1
100
1700
74
1

PooledVariable
SM OKING
SM OKING>0UNIV
RISKAVINCOM E
AGE
CHILD

0
2.753
0
0
7.000
19
0

42

M ax
46.62246.622
1
100
3000
74
1

Appendix C(c): Summary statistics of SMOKING and control variables (female samples)
2005
Variable
SM OKING
SM OKING>0UNIV
RISKAVINCOM E
AGE
CHILD

Obs
1577
250
1529
1556
1220
1584
1564

M ean
2.193
13.8320.122
51.298215.900
48.1190.834

Std. Dev.
6.223
9.136
0.327
19.749156.104
12.969
0.372

M in

Obs
1983
271
1917
1956
1585
1989
1950

M ean
2.038
14.9160.125
50.866210.64748.3350.833

Std. Dev.
6.054
8.734
0.331
19.289163.845
13.108
0.373

M in

Obs
1647
212
1617
1637
1367
1663
1648

M ean
2.081
16.1660.126
50.929217.147
49.4280.839

Std. Dev.
6.649
10.7770.332
19.442168.228
12.848
0.368

M in

Obs
2131
270
2075
2111
1829
2139
2114

M ean
1.785
14.0920.142
50.715
222.61049.0060.832

Std. Dev.
5.748
9.355
0.349
19.288178.19313.229
0.374

M in

Obs
7338
1003
7138
7260
6001
7375
7276

M ean
2.008
14.6880.129
50.929216.84248.7300.834

Std. Dev.
6.144
9.487
0.336
19.420167.856
13.062
0.372

M in

0
2.739
0
0
11.667210

M ax
43.06043.060
1
100
1500
71
1

2006
Variable
SM OKING
SM OKING>0UNIV
RISKAVINCOM E
AGE
CHILD

0
2.777
0
0
11.667190

M ax
42.99642.996
1
100
1500
72
1

2007
Variable
SM OKING
SM OKING>0UNIV
RISKAVINCOM E
AGE
CHILD

0
2.710
0
0
17.50020
0

M ax
44.24844.248
1
100
1700
73
1

2008
Variable
SM OKING
SM OKING>0UNIV
RISKAVINCOM E
AGE
CHILD

0
2.734
0
0
10.00020
0M ax
43.20143.201
1
100
1700
74
1

PooledVariable
SM OKING
SM OKING>0UNIV
RISKAVINCOM E
AGE
CHILD

0
2.710
0
0
10.000
19
0

43

M ax
44.24844.248
1
100
1700
74
1

Appendix D: Correlations between S MOKING and control variables
2005

M ALE
UNIV
RISKAVINCOM E
AGE
CHILD

2006

All
0.383 ***
(0.000)-0.012(0.506)-0.109 ***(0.000)0.041 **
(0.047)-0.039 **(0.033)-(0.055) ***(0.003)

SM OKING
M ale

FemaleM ALE-0.127 ***(0.000)-0.137 ***(0.000)0.060 **
(0.043)-0.060 **(0.025)-(0.023)(0.393)-0.052 **(0.044)-0.041(0.107)-0.051 *(0.078)-0.124 ***(0.000)-(0.047) *(0.067)UNIV
RISKAVINCOM E
AGE
CHILD

2007

M ALE
UNIV
RISKAVINCOM E
AGE
CHILD

All
0.380 ***
(0.000)-0.021(0.202)-0.100 ***(0.000)-0.001(0.967)-0.033 **(0.044)-(0.043) ***(0.009)SM OKING
M ale

Female-0.136 ***(0.000)-0.112 ***(0.000)-0.016(0.536)-0.052 **(0.029)-(0.008)(0.734)-0.075 ***(0.001)-0.059 ***(0.009)-0.047 *(0.061)-0.112 ***(0.000)-(0.050) **(0.028)

SM OKING
M ale

Female-0.118 ***(0.000)-0.099 ***(0.000)-0.016(0.515)-0.042 *(0.071)(0.038)(0.102)-0.072 ***(0.001)-0.087 ***(0.000)-0.035(0.140)-0.096 ***(0.000)-(0.017)(0.442)

2008

All
0.360 ***
(0.000)-0.019(0.305)-0.091 ***(0.000)-0.003(0.879)-0.037 **(0.040)-(0.029)(0.114)

SM OKING
M ale

FemaleM ALE-0.130 ***(0.000)-0.102 ***(0.000)-0.010(0.718)-0.066 **(0.013)(0.002)(0.940)-0.065 ***(0.009)-0.059 **(0.017)-0.037(0.175)-0.097 ***(0.000)-(0.047) *(0.056)

SM OKING
M ale

Female-0.130 ***(0.000)-0.111 ***(0.000)0.001(0.927)-0.056 ***(0.000)(0.003)(0.820)-0.067 ***(0.000)-0.062 ***(0.000)-0.042 ***(0.001)-0.107 ***(0.000)-(0.039) ***(0.001)UNIV
RISKAVINCOM E
AGE
CHILD

All
0.344 ***
(0.000)-0.010(0.528)-0.099 ***(0.000)-0.013(0.446)-0.025(0.122)-(0.003)(0.868)PooledM ALE
UNIV
RISKAVINCOM E
AGE
CHILD

All
0.366 ***
(0.000)-0.017 **(0.050)-0.099 ***(0.000)0.003(0.719)-0.034 ***(0.000)-(0.032) ***(0.000)Notes: The tables show the correlation coefficients of cigarette consumption with control variables. Samples include nonsmokers.
P-values are in parentheses.
*, **, *** denote statistical significance at the 10%, 5%, and 10% levels, respectively.

44

Appendix E: Tax hike s, smoking mode ration, and increasing rate s of tax re ve nue
Model (1): The marginal effect of tax= -0.416
before tax hikes

after tax hikes
JPY 500(USD 4.67)
JPY 10(USD 0.09)
18.880
22.4%
10.7%
73.2%

JPY 700(USD 6.53)
JPY 20(USD 0.19)
14.72522.4%
10.7%
107.2%

JPY 1000(USD 9.33)
JPY 35(USD 0.33)
13.95615.6%
37.8%
107.9%

Model (2): The marginal effect of tax= -0.423
before tax hikes
JPY 300
Cigarette price per pack
(USD 2.80)
JPY 0⊿TAX per cigarette(USD 0)
Averaged cigarette consumption
20.873
Smoking rates
25.1%
Smoking cessation rates for smokers
0.0%
Increasing rates of revenues
0.0%

JPY 500(USD 4.67)
JPY 10(USD 0.09)
18.807
22.4%
10.7%
72.6%

after tax hikes
JPY 700(USD 6.53)
JPY 20(USD 0.19)
14.57922.4%
10.7%
105.1%

JPY 1000(USD 9.33)
JPY 35(USD 0.33)
13.701
15.6%
37.8%
104.1%

Model (3): The marginal effect of tax= -0.315
before tax hikes
JPY 300
Cigarette price per pack
(USD 2.80)
JPY 0⊿TAX per cigarette(USD 0)
Averaged cigarette consumption
20.873
Smoking rates
25.1%
Smoking cessation rates for smokers
0.0%
Increasing rates of revenues
0.0%

JPY 500(USD 4.67)
JPY 10(USD 0.09)
19.890
22.4%
10.7%
82.5%

after tax hikes
JPY 700(USD 6.53)
JPY 20(USD 0.19)
16.74522.4%
10.7%
135.6%

JPY 1000(USD 9.33)
JPY 35(USD 0.33)
17.491
15.6%
37.8%
160.5%

Cigarette price per pack
⊿TAX per cigarette
Averaged cigarette consumption
Smoking rates
Smoking cessation rates for smokers
Increasing rates of revenues

JPY 300(USD 2.80)
JPY 0(USD 0)20.873
25.1%
0.0%
0.0%

Notes: This tabels show the predicted smoking status and increasing rates of government's revenues responding to tax
hikes by JPY 10, 20, and 35 (around USD 0.09, 0.19, and 0.33) per cigarette. Estimated coefficients of TAX in model (1),(2), and (3) for full samples are used for the predictions, respectively. The marginal effects are measured around meanvalues of smokers in 2008. The rows of "Cigarette price per pack" show the hypothetical prices per 20-cigarette packafter tax hikes calculated by (20*⊿TAX per cigarette). The rows of "⊿TAX per cigarette" show the increased rates oftax per cigarette. The rows of "Averaged cigarette consumption", "Smoking rates", and "Smoking cessation rates for
smokers" show the resulting status of cigarette consumption after tax hikes. The rows of "Increasing rates of revenues"
show the net increasing rates of government's revenues by hiking cigarette tax.

45

Appendix F: Tax hikes and the increasing rates of tax revenue

increasing rates of tax revenue

Panel (A): The marginal effect of TAX= -0.416 (Model (1))

(Unit: JPY)

200%
150%
100%
50%
0%
-50%

0

20

40

60

80

100

120

140

160

-100%
⊿TAX
Panel (B): The marginal effect of TAX= -0.423 (Model (2))(Unit: JPY)

increasing rates of tax revenue

200%
150%
100%
50%
0%
-50%

0

20

40

60

80

100

120

140

160

-100%
⊿TAX

Panel (C): The marginal effect of TAX= -0.315 (Model (3))
increasing rates of tax revenue(Unit: JPY)
200%
150%
100%
50%
0%
-50%

0

20

40

60

80

100

120

140

160

-100%
⊿TAX

Notes: This figures show the predicted increasing rates of government's revenues responding to tax hikes. The horizonalaxes indicate the increased rates of tax per cigarette. The vertical axes indicate the net increasing rates of government's
revenues by hiking cigarette tax in JPY terms. The estimated coefficients of TAX in model (1), (2), and (3) for fullsamples are used for the predictions, respectively. The marginal effects are measured around mean values of smokers in2008.

46