HIAS-E-108

Does Restricting the Availability of Cigarettes Reduce
Smoking?
Ryota Nakamura(a), Ying Yao(b)(a) (b) Hitotsubashi Institute for Advanced Study, Hitotsubashi University

August, 2021

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
tel:+81 42 580 8668 http://hias.hit-u.ac.jp/
HIAS discussion papers can be downloaded without charge from:https://hdl.handle.net/10086/27202
https://ideas.repec.org/s/hit/hiasdp.html

All rights reserved.

Does Restricting the Availability of Cigarettes
Reduce Smoking?∗
Ryota Nakamura† and Ying Yao‡
Draft Version: August 30, 2021

Abstract
This paper estimates the causal effects of restricting cigarette availability on purchasing patterns. We design a research strategy that enables the estimation byleveraging the impact of an unforeseen discontinuation of products because of the2011 Great East Japan Earthquake. We analyze nationally representative homescanner data in Japan and find that making certain products unavailable leads
smokers to switch to products with less tar and nicotine and purchase 32 percent
fewer cigarettes per month. As a result, the total amount of tar and nicotine inpurchased cigarettes has decreased by 43 and 30 percentage points, respectively.
Such effects persist over the years.

Keywords: Tobacco consumption; Supply restriction; Product availability; Natural experiment; JapanJEL Classification: D12, I12, I18∗

This work is supported by JSPS KAKENHI Grant Number JP19K13717 and Health and Labour
Sciences Research Grant Number 20FA1022. The views expressed in this article are those of the authorsand do not necessarily reflect those of the funders.
†Hitotsubashi University. Email: ryota.nakamura@r.hit-u.ac.jp‡
Hitotsubashi University. Email: ying.yao@r.hit-u.ac.jp

1

Introduction

Smoking is a major public health concern that kills millions of people every year. Governments in many countries have strived to constrain smoking and other risky behaviors by
restricting access to unhealthful products. The strategies involve partial bans on products
under specific conditions, e.g., minimum age restrictions on purchases (Cook and Moore,
2001; DiNardo and Lemieux, 2001; Abouk and Adams, 2017), banning smoking in public
places and workplaces (Evans et al., 1999; Farrelly et al., 1999; Wakefield et al., 2000),
and restricting hours on sales (Bernheim et al., 2016). Outright bans on a product areamong the most aggressive and rare classes of regulatory interventions, and their impact
on health behavior is largely unknown (Miron and Zwiebel, 1991; Cawley and Ruhm,
2011). In this study, we demonstrate how restricting the availability of certain tobacco
products affects smokers’ choice and consumption of tobacco. We show that such anintervention can achieve a sustainable reduction in smoking.
Endogenous consumer choices present major challenges to causal inferences between
restricting product availability and consumption. Choices or preferences of tobacco are
typically formed over time and associated with a complex system of individual, social,and financial factors. Methodologically, a randomized control trial that restricts access tocertain marketed products is impractical to implement. In this study, we present a uniqueobservational research design that exploits an unforeseen discontinuation of tobacco products because of factory shutdowns after a large natural disaster—the 2011 Great East
Japan Earthquake. The unplanned product discontinuation prompted changes in the purchasing patterns of tobacco by smokers who consumed these products. To quantify the
impacts of the product discontinuation on tobacco choice and consumption, we use largescale, nationally representative home scanner data from Japan, which cover the daily
grocery transactions of 75,817 consumers from April 2010 to December 2014. We focus
on a sample of 16,533 cigarette purchasers. We apply a difference-in-differences (DID)
approach that compares tobacco purchases of the affected smokers before and after thestockout of the discontinued tobacco products with smokers who were unaffected by thediscontinuation. In particular, we investigate the dynamic process of choice adjustment
1and the corresponding consequences for product choice and the total consumption of thesubstances.
Our empirical analysis provides three main findings. First, affected consumers switched
to products with less tar and nicotine following the stockout of discontinued products.
As a result, the tar and nicotine levels in a cigarette decreased by 12 and 3 percentage
points, respectively. The range of product choices also changed significantly, with theupper and lower bounds of the selected products shifting toward lower-tar products. Second, although affected consumers searched for new alternatives, they ended up choosing
products from the remaining familiar options. Their new regular choices stabilized after
a short adjustment period. Third, monthly cigarette consumption exhibited a sustained
decline following the stockout. The number of cigarettes purchased decreased by approximately 30 cigarettes or 32 percentage points. In addition, because of product switching,
the total tar and nicotine in purchased cigarettes fell by 278 mg (or 43 percentage points)
and 23 mg (or 30 percentage points), respectively.
We also speculate on several pathways that may influence cigarette consumption after
product discontinuation. First, we investigate the idea that the discontinuation of aconsumer’s most preferred products could have a tangible impact on their behavioral
change. We find that affected consumers shifted to products with less tar and nicotine
regardless of whether the discontinued products were among the most purchased. Second,
the level of addiction may reflect the difficulty of reducing cigarette consumption. It turns
out that heavy and light smokers made similar product switches and reduced cigarette
consumption. Third, as contributors to disparities in smoking, educational attainment
and income may explain part of the behavioral change concerning cigarette purchases.
However, our results show that the reduction in purchases is evident among all educationand income groups. Fourth, we evaluate the assumption that behavioral change mayvary by residence, as consumer exposure to the earthquake and subsequent supply shocks
differed. We find that the stockout of discontinued products uniformly affected consumers
across the country. Consumers in earthquake-stricken areas displayed the same behavioral
changes in terms of cigarette purchases as those in other areas.
2This paper adds to the broader literature on the economics of preventing smoking andthe use of other undesirable substances. Prior studies have examined the impact of supply
restrictions on alcohol consumption and related health outcomes (Miron and Zwiebel,
1991; Jacks et al., 2021), methamphetamine supply and use (Dobkin and Nicosia, 2009),
sales of flavored cigarettes and e-cigarettes (Abouk and Adams, 2017; Courtemanche
et al., 2017). Our contribution to the literature is four-fold. First, we investigate a
supply restriction of some (not all) products in the tobacco market. Rather than treatingproducts as homogenous, we use detailed product information to extensively analyze
the substitution of and the switch between products. Furthermore, by exploiting five
years of daily observations of cigarette purchases, we demonstrate how the choice andconsumption of tobacco products have adjusted over time following the supply restriction.
Second, we estimate the effect of supply restrictions in the context of limited supply
and not price increases. In previous studies on supply restrictions, price played a crucialrole in the change in consumption as supply restrictions translate to rising demands andthus an increase in prices (Dobkin and Nicosia, 2009). In our case, strict regulation by
the government kept tobacco prices fixed, even when cigarettes were in short supply. This
unique environment precludes the bias in policy effects associated with the price elasticityof the demand for tobacco.
Third, we adopt a research design that minimizes biases because of “policy endogeneity” — the timing of the product restriction determined partially by the prevalenceof smoking and/or relevant health outcomes (Cawley and Ruhm, 2011). In our case,the supply restriction was not determined by the regulator; it was tied entirely to an
unforeseen plant shutdown caused by a devastating earthquake.
Finally, in terms of policy implications, we find a persistent effect of supply restrictions
on reducing smoking across the population, which is barely documented in real-world
evidence (Hollands et al., 2019). Prior research suggests that this long-term effect exists
only in specific populations (Terry-McElrath et al., 2015), is attenuated by the presenceof alternatives (Courtemanche et al., 2017), or is temporary (Dobkin and Nicosia, 2009).
From a policy perspective, this paper also adds to the recent debate on major tobacco
3control policies. The use of affordability policies such as price increases or taxation iscommon in reducing tobacco use and smoking rates. However, policy effects are often
temporary and induce compensatory behavior. Smokers often gradually return to pre-tax
consumption levels after tax hikes. Moreover, they tend to switch to cigarettes with more
tar and nicotine as they reduce the number of cigarettes they consume, which greatly
offset the health effects of tax increases (Evans and Farrelly, 1998; Farrelly et al., 2004;
Adda and Cornaglia, 2006; Cotti et al., 2016). In contrast to tax increases, imposing
supply restrictions can alter health behavior in the long term without triggering compensatory behavior. Our findings provide evidence and insights for the implementation ofsimilar policies to control tobacco use.
The rest of the paper proceeds as follows. Section 2 outlines the context of the research
design, while Sections 3 and 4 describe the data and empirical strategies, respectively.
In Section 5, we present the impact of product discontinuation on cigarette purchases.
The potential drivers resulting in behavioral change are discussed in Section 6. We thenconclude the work in Section 7.

2

Background2.1

Tobacco Market in Japan

Japan is one of the 10 countries with the greatest cigarette consumption (Hoffman et al.,2019). Although sales have fallen in recent years because of declining smoking rates,
tobacco contributes 2 trillion yen annually in taxes (about 1.6 percent of the nationaltax revenue in 2019) and dividends to the Japanese government. Japan Tobacco (JT)is the only tobacco manufacturer in Japan, controlling over 60 percent of the domestic
sales in Japan (Japan Tobacco, 2018). It was a state-owned monopoly, with the Ministryof Finance owning 67 percent of the company, until its privatization in 1985. Whileits ownership share continues to decline,1 the Ministry of Finance remains the primary
stakeholder, and it controls tobacco manufacturing, distribution, and pricing. By law,tobacco companies and retailers must obtain licenses from the Ministry, and they cannot
1This share declined to 50 percent in 2004 and then to 33 percent in 2016 (MacKenzie et al., 2017).

4change their product prices without permission. In the past decades, tobacco prices havechanged only in response to changes to the tobacco excise tax or the consumption tax.2
As a result, cigarette prices are nearly identical across products, and there is no pricediscrimination across regions (Figure B.1(a) in the Appendix). Furthermore, these prices
remained fixed even when the market faced a short supply after the earthquake in 2011.
In addition, there is no known black market for tobacco in Japan.
Despite the small price gap between products, products are excessively diversified,from ultra-low-tar (1 mg per cigarette) to high-tar cigarettes (up to 42 mg per cigarette).
The nicotine content in cigarettes increases with the tar content (Figure B.1(b)). The
typical products in the market are gradually shifting from high-tar to low-tar products.
The weighted average of tar (nicotine) in a cigarette has decreased from 9.5 (0.78) mg
in 1994 to 6.8 (0.56) mg in 2019 (Figure B.1(c)). Among the 100 best-selling products,
1mg-tar cigarettes have the highest market share at about 24 percentage points. The
runners-up are cigarettes with 8 mg and 6 mg of tar, together making up approximately
27 percent of the market. In the following ranking, cigarettes with a tar content of 14
mg and 3 mg together account for a total of 16 percent of the market (Figure B.1(d)).

2.2

JT Products and Supply

As of 2020, JT had 11 major brands, nearly half the number it had in 2011. Products
range from low to high tar (nicotine) content in each brand, and they each target different
market segments (See Figure B.2 in the Appendix). JT only occasionally discontinues
products, and the product lineup has not changed significantly. It has only increased from96 in 2011 to 102 in 2020. Some of those terminated were long-established products, whileothers were on the market for just a brief period. Since 2000, JT has removed an averageof four products from its catalog each year. Notably, JT discontinued 23 products in
2011, when the earthquake occurred. Following this event, JT rarely discontinued products. Since 2016, JT has resumed discontinuing products, with up to eight discontinued
products per year (see Figure 1).
2

Recent tax hikes occurred in April 2010 and December 2014. The first one was the excise tax increasein October 2010. The second one was the consumption tax, which rose from 5 percent to 8 percent inApril 2014.

5

25
15
10

Number

20

23

8
7

5

6

6

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4
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3

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3

2

2
1

1

1

0

20
00
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20
02
20
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20
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20
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20
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20
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20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19

0

0

8

7

Figure 1: The number of discontinued JT products
Notes: This chart shows product discontinuations from 2000-2019 for the 23 brands listed
on JT’s press release on May 11, 2011. See https://www.jt.com/media/news/2011/pdf/
20110325_02.pdf for details.

Product discontinuations are usually planned, with the exception being those associated with the Great East Japan Earthquake in 2011. The earthquake and subsequenttsunami caused JT to lose 30 percent of its production capacity as two JT cigarette manufacturing plants and another two factories producing semi-finished products and filters
were shut down. Unable to consistently supply all of its products, JT announced that
it would temporarily suspend shipments from March 30 to April 10 to increase the production and inventory of 25 key products, which account for approximately 65 percent of
its total sales (Japan Tobacco, 2011). As a result, JT cigarettes were in extremely short
supply on the market, causing panic among smokers. Starting on April 11, JT gradually
restored the supply of key products. On May 11, two months after the earthquake, JTcommitted to resuming the supply of 73 products by early August. However, it eventually discontinued 23 products that possessed low market shares before the earthquake
and whose sales were expected to decline at an accelerated rate over the next few years(Japan Tobacco, 2011).
These discontinued products were sold nationwide, except for two geographically re6

stricted products. They had been on the market for various lengths of time; nearly halfof them had a product history of more than 10 years. Despite the gap in market share,
discontinued products, key products, and other JT products were very similar in average
tar and nicotine content, containing around 7.5 mg per cigarette. The typical (median)price of a 20-cigarette pack was identical for three types of products. In terms of average
price, the discontinued products were slightly higher than the key and other surviving
products by about 10–20 yen per pack (see Table 1).
Table 1: Attributes of JT products (as of May 2011)
Attribute

Mean

SD

Median Min. Max.

Discontinued products Tar per cigarette (mg)(n = 23)
Nicotine per cigarette (mg)Year of release
Price per pack (20 cigarettes)

7.3
0.6
1997
424.3

4.4
0.3
12
19.5

7
0.6
2000
410

1
0.1
1970
390

17
1.2
2010
470

Key products(n = 25)

Tar per cigarette (mg)
Nicotine per cigarette (mg)Year of release
Price per pack (20 cigarettes)

7.7
0.6
1991
402.8

5.7
0.4
16
49

8
0.6
1996
410

1
0.1
1957
240

19
1.4
2011
440

Other JT products§(n = 46)

Tar per cigarette (mg)
Nicotine per cigarette (mg)Year of release
Price per pack (20 cigarettes)

7.5
0.5
1994
412.9

6.9
7
22
62.9

6
0.5
2003
410

1
0.1
1906
200

28
2.3
2011
600

Notes: The product list is extracted from JT’s press release in May 2011 (See https://www.jt.com/media/news/2011/pdf/20110512_10.pdf for details). §Two smokeless products (Zerosytle) are excluded because information about tar and nicotine is not available.

3

Data and Stylized Facts

3.1

Data

We use nationally representative consumer scanner data from the SCI® Nationwide Consumer Panel Survey of Japan from April 2010 to December 2014. SCI® is analogous toother leading home scanner databases, such as Nielsen Homescan and Kantar WorldPanel.
The Intage Group began recruiting participants in December 2009 through web banners
and job search websites. In return for participation, the participants received reward
points from a website belonging to the Intage Group (https://www.cue-monitor.jp/),which could be exchanged for cash and various gift cards. The data collection is based
7

on the population census. The sampling procedure uses a quota technique that draws asample of individuals with the same proportions of individuals as the entire population
regarding demographic characteristics such as sex, marital status, and age.
The baseline survey was conducted in April 2010, and it covered 21,607 individuals
from 11 regions from northern to southern Japan (Hokkaido, Tohoku, North Kanto,
South Kanto, Hokuriku, Tokai, Kinki, Chugoku, Shikoku, Kyushu, and Okinawa).3 In
the follow-up surveys, the data company recruited additional individuals in the samemanner as the baseline survey. As a result, the sample size comprised 55,790 individuals
in 2014. Participants remain in the panel if they comply with the reporting rules set
by the Intage Group, which regularly monitors the quality of the submitted data. New
participants with the same sex, marital status, age, and residential area were sought toreplace those who withdrew or failed to meet the reporting criteria.
After each purchase, participants are required to use a mobile device to scan the barcodes on products and enter the date of purchase and receipt information (including unit
price, number of items purchased, the total amount paid, and store name) on the survey website. Using the scanned barcodes, the Intage Group collected product attributes,
such as brand, package size, manufacturer, tar and nicotine content, flavor, and cigarettesize. The collection of information on cigarettes’ tar and nicotine content aligns with thetesting procedure of the International Organization for Standardization. We find thatthree discontinued products are not included in the dataset. We also choose to exclude
heated, cut, and smokeless tobacco (2 percent of the sample) from the dataset becauseof a lack of tar and nicotine information.

3.2

Cigarette Purchasers and Their Characteristics

By pooling all participants in the survey between April 2010 and December 2014, we
obtain a final sample of 75,817 consumers. Among these consumers, 16,533 purchased
at least one pack of cigarettes during the survey period. The average length of their
participation in the survey was 32.8 months (ranging from 1 to 57 months).
3

The definitions of regions follow the classification of the Organization for Economic Cooperation andDevelopment (OECD).

8The cigarette purchasers vary in age, sex, family size, education, household income,and occupation (see Table A1 in the Appendix). Male and female consumers are almost
equally distributed, with an average age of approximately 44. Nearly two-thirds of consumers are married, and the average household size is around three people. Purchasers
with secondary education or lower represent the largest proportion (41 percent), followedby highly educated purchasers (35 percent) and those with a junior college education orequivalent (24 percent). Nearly half of the purchasers have an annual household incomeof less than 5.5 million yen, approximately equal to the national average annual income(Ministry of Health, Labor and Welfare, 2009). Employed consumers comprise 75 percent of the purchasers, of which 65 percent are white-collar workers. Blue-collar workers,
students, unemployed, and others groups constitute another 35 percent of the purchasers.
We aggregate monthly transactions for each cigarette purchaser and identify that 664
of them bought discontinued products before the discontinuation notice was issued in
May 2011. These affected consumers tended to be older, male, married, better educated,and employed, and they purchased more cigarettes than those who did not purchase thediscontinued products. We present the purchasing trends of these two groups in Figure 2.
The two groups have not differed in their preferences for tar content, except for theperiods after product discontinuation. The split in preferences between the two groups
was evident a few months after the discontinuation notice. Affected consumers shiftedto low-tar cigarettes, while unaffected consumers had stable preferences over time (Figure 2(a)). Despite the divergence in preferences, the price each group paid for a cigarette
overlapped, reflecting the price homogeneity of tobacco products in Japan (Figure 2(b)).
Unlike product preferences, the gap in purchase volume between the two groups has persisted (Figure 2(c) and 2(d)). This gap varied with consumer response to supply shocks,
such as tax hikes and product discontinuations. Before the tax increases, both groups
showed signs of pre-tax stockpiling, with consumption gradually returning to pre-tax
levels after a brief post-tax slump. In contrast, the consumption of affected consumers
slowly declined when their previously purchased products became unavailable, whereas
the consumption of unaffected consumers remained stable.
9

25

8
Excise tax

Stockout

Affected

8

12
14
m

20

3

14
m
20

10

14
m
20

4

AffectedControl

Control(c) Number of cigarettes

m
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8

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Affected

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200

Cigarettes
400
600

Total tar amount (mg)
2000
4000

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6000

(b) Price per cigarette

1000(a) Tar content per cigarette

13
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Control

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4

10

Tar per cigarette (mg)5
6
7Price per cigarette (Yen)15
20

Consumption
tax

Control(d) Total amount of tar

Figure 2: Cigarette purchases over time
Notes: The two shaded areas represent a few months before and after the two tax increases.
The dotted lines represent the month when discontinued products became out-of-stock.

4

Empirical Strategy

We apply a DID approach to estimate the effect of product discontinuation on tobacco
consumption. The regression takes the following format:

Yit = β0 + β1 T reati + β2 P ostt + β3 T reati × P ostt + θControlsit + ηi + λt + εit(1)where indices i and t represent the individual and month, respectively, and Yit represents
consumer product choices or purchase volume. In addition, T reati is a dummy variable
that indicates whether individual i had purchased discontinued products before the discontinuation notice (May 2011). Moreover, P ostt is a dummy variable that is equal to10

1 if the discontinued products were out of stock, and it is 0 otherwise. The transaction data suggest that the stockout of discontinued products occurred in August 2011,
although sporadic purchases have occurred since then (see Figure B.3 in the Appendix).
We, therefore, designate September 2011 as the beginning of the post-stockout period.
The coefficient (β3 ) captures the treatment effect of product discontinuation on consumer
choices. The control variables include a series of individual characteristics, including age,gender, education, household income, family size, and the prefecture of residence. Finally,
the fixed effects of the individual (ηi ) and month (λt ) are considered.
The validity of the DID analysis requires that 1) the allocation of the treatment is
not determined by smoking behavior, 2) in the absence of product discontinuation, thepurchase choices of treated and comparison consumers would follow the same trend, and3) the composition of the treatment and comparison groups are stable. The remainder ofthis section tests these assumptions and provides evidence that supports our use of thisresearch strategy.

4.1

Assignment of Treatment

For our DID approach to be valid, smokers’ behavior should not determine productdiscontinuation. Product discontinuation is ubiquitous in firm behavior. Firms, includingJT, often make strategic decisions about the timing and range of product discontinuation.
Such decisions raise concerns about “policy endogeneity” (Cawley and Ruhm, 2011), i.e.,
whether our treatment assignment is affected by consumer behavior. The process of
selecting products to discontinue may not be random, especially when JT was faced withlimited production capacity. We, however, argue that the individual smokers’ behaviors
did not influence JT’s decision in this case.
In terms of the timing of the discontinuation, it is unlikely that JT’s product rationalization shortly after the earthquake was planned. In contrast to a planned discontinuation, JT’s post-earthquake decision was driven by an unforeseen shortage of production
capacity that made it impossible to sustain their original lineup of products. As shownin Figure 1, the number of products discontinued as a result of planning had been small
11

before 2011 — typically around four per year. The significantly higher number of discontinued products in 2011 reflects the unintended consequences of supply disruptions.
JT has publicly announced that market share — measured by aggregate sales — determines product rationalization, especially with regard to product range. This criterion
meets the main interest of the majority stakeholder — the Ministry of Finance. Since thetax per cigarette in Japan is fixed across products, product prioritization using aggregate
sales rules can maximize tax revenue. Admittedly, we cannot rule out the possibility that
JT chose to fine-tune the product range based on its prediction of consumer responses.
However, the fact that JT used aggregate sales as the criterion to discontinue products
implies that the behavior of individual smokers was unlikely to play a crucial role indetermining the range of products to be discontinued.
Furthermore, the behavior of individual smokers can hardly respond to aggregate
sales. Our data confirm that a high market share does not guarantee more individual
purchases. For example, for some treated consumers, the discontinued products—despite
their low market share—accounted for all of their purchases. The discontinued products,
on average, accounted for 24 percent of purchases by treated consumers, which was higher
than some other JT products (see Table A2 in the Appendix).

4.2

Common Trends in Cigarette Purchases

We perform an event study analysis to examine whether the trends in outcomes are thesame in the treatment group and control group in the absence of treatment exposure.
The regression uses a slightly modified version of equation (1):Yit = β0 +15X

δj Di,t−j + θControlit + ηi + λt + εit

(2)

j=−15

We normalize the timing of the stockout (August 2011) to period 0 and set the analysis
period to 15 months before (−15 < j < 0) and after (0 ≤ j ≤ 15) the stockout. The
dummy variable Dit = 1[T reati = t] takes the value 1 in the month of stockout and zerootherwise. The estimated coefficient for δj represents the effects of supply shock on thetreated consumers at month j. The indicator Di,t−1 is omitted to reflect the baseline12difference between consumers when the stockout occurred and when they did not occur.
We expect δj = 0 for −15 < j < 0 if pre-existing trends in the outcome are absent.
Figure 3 presents the event study graphs of the impact of product discontinuation on
product choices, cigarettes purchased, and total amount of tar (nicotine) in purchased
products. The product choice is indicated by the cigarette’s tar (nicotine) content, which
is obtained by dividing the total amount of tar (nicotine) by the total number of cigarettes.
Here we only visualize the results for tar because, as shown in Figure B.1(b), tar andnicotine content in a cigarette are positively correlated. A vertical dashed line indicatesthe month of the stockout in all subfigures. These figures suggest that there are no
systematic differences in pre-existing trends except for the period between October 2010
and February 2011. The excise tax increase in October 2010 may explain the sharp
decline in purchases. To eliminate the interference of the two tax hikes, we restrict the100
Cigarettes

0Estimated coefficient

20
1
20 0m
1 5
20 0m
1 7
20 0m
8
20 10m
10 9
20 m
1 10
20 0m
10 11
20 m1
1 2
20 1m
1 1
20 1m
1 2
20 1m
1 3
20 1m
1 4
20 1m
1 5
20 1m
1 6
20 1m
8
20 11m
1 9
20 1m
11 10
20 m
11 11
20 m1
1 2
20 2m
1 1
20 2m
1 2
20 2m
1 3
20 2m
1 4
20 2m
1 5
20 2m
1 6
20 2m
1 7
20 2m
8
20 12m
12 9
m
11

−100

0

−.5
−1

20
1
20 0m
1 6
20 0m
1 7
20 0m
8
20 10m
1 9
20 0m
1 10
20 0m
10 11
20 m1
1 2
20 1m
1 1
20 1m
1 2
20 1m
1 3
20 1m
1 4
20 1m
1 5
20 1m
1 6
20 1m
8
20 11m
1 9
20 1m
1 10
20 1m
1
11 1
20 m1
1 2
20 2m
1 1
20 2m
1 2
20 2m
1 3
20 2m
1 4
20 2m
1 5
20 2m
1 6
20 2m
1 7
20 2m
8
20 12m12 9
m
1095% CI

Estimated coefficient(b) Number of cigarettes

0
−1000
1
20 0m
1 6
20 0m
10 7
20 m
8
20 10m
1 9
20 0m
10 10
20 m
10 11
20 m1
1 2
20 1m
1 1
20 1m
1 2
20 1m
1 3
20 1m
1 4
20 1m
1 5
20 1m
1 6
20 1m
8
20 11m
11 9
20 m
11 10
20 m
11 11
20 m1
1 2
20 2m
1 1
20 2m
1 2
20 2m
1 3
20 2m
1 4
20 2m
1 5
20 2m
1 6
20 2m
1 7
20 2m
12 8
m
9

−500

Total tar amount (mg)

500

1000

(a) Product choice

95% CI20

Tar per cigarette (mg)

.5

200

analysis to periods between March 2011 and February 2014.

Estimated coefficient

95% CI(c) Total tar in purchased cigarettes

Figure 3: Event-study analysis of cigarette consumption
Notes: The dashed line represents the month in which stockout occurred. The circles andspikes represent the estimated coefficients and 95% confidence intervals, respectively.

13

4.3

Comparability between Affected and Comparison Groups

Another challenge to the analysis is compositional changes in consumer characteristics,
which may be associated with changes in treatment exposure. As illustrated in Table A1of the Appendix, the affected and comparison consumers have different socioeconomic
characteristics. Despite these differences, the identification strategy is valid as long as the
distribution of these covariates is stable over time. The design of the survey stated thatthe composition and distribution of the survey participants were stable. Furthermore, acomparison of the means between the two groups over the study periods confirms thatcompositional changes are unlikely to exist (Table A3 in the Appendix).
Given the group differentials and potential bias because of confounding, we also incorporate propensity scores into our DID estimations. We restrict transactions to thepre-earthquake period from April 2010 to February 2011 and obtain a sample of consumers in the treated and control groups. A logit regression estimates the propensity
score of an individual being affected by the product discontinuation. The control variables include individual characteristics and monthly purchase intensity, i.e., the average
number of cigarettes and average tar (nicotine) content per cigarette in purchased products. We apply the kernel matching method (Heckman et al., 1998) to select baseline
individuals with a similar probability of being affected by the stockout. The choice of
bandwidth in kernel matching is based on the rule-of-thumb bandwidth for Epanechnikov
kernels (Silverman, 1986). The diagnostic test after matching suggests a balance between
the treated and control groups. Individuals in the two groups do not exhibit statisticallysignificant differences in the means of the covariates (Figure B.4 in the Appendix). The
DID estimation is then performed using the sample of matched consumers. Additional
tests using matched samples confirmed the absence of pre-existing trends in pre-stockout
periods from March 2011 to August 2011 (Figure B.5 in the Appendix).

14

5

Results

5.1

Consumer Choices

Like other consumers, smokers often develop a set of product choices based on theirpast purchases. This set of choices includes multiple products with varying tar (nicotine)
content. Consumer preferences for each item in the set may vary. For some consumers, theloss of one option in the set may not affect their purchasing decisions; they can continue
to select items from their remaining choices. Others may search for new alternatives toreplace the lost item(s). Either pattern will drive a shift in choices, either to productswith the same or different tar (nicotine) content.
To identify changes in consumer choices, we compare the tar (nicotine) content percigarette before and after the stockout. Dependent variables, such as average, maximum,and minimum tar (nicotine) per cigarette, are constructed based on monthly transactions.
The average indicator is a weighted index representing the tar (nicotine) content of theconsumer’s most purchased products.4 We present the results in Table 2. The estimatesof different methods in column 1 show that the tar content per cigarette decreased by 12
percentage points after the stockout, while the reduction in nicotine content per cigarette(column 2) was 3 percentage points. In addition to the average content per cigarette, theupper bounds of tar and nicotine content per cigarette (columns 3 and 4) dropped by 16
and 4 percentage points, respectively. Reductions in the lower bounds of tar and nicotine
content per cigarette (columns 5 and 6) are not as pronounced as the upper bounds, atapproximately 7 and 1.1 percentage points, respectively.

4

The data also show that the two indicators coincide with each other.

15

Table 2: Changes in product choices
Substance per cigarette

Upper bounds

Lower bounds

Tar
(1)

Nicotine
(2)

Tar
(3)

Nicotine
(4)

Tar
(5)

Nicotine(6)-0.12***(0.020)-0.026***(0.0050)-0.16***(0.022)-0.044***(0.0058)-0.058***(0.020)-0.0077(0.0049)-1.35***(0.074)-0.30***(0.018)-1.41***(0.078)-0.32***(0.020)-1.24***(0.069)-0.27***(0.017)

Observations
366,865
Mean of Dep. Var.
3.34
SD of Dep. Var.
4.64
Panel B. PSM-DID
T reat × P ost-0.12***(0.021)

366,8650.28
0.37

366,865
3.64
4.99

366,8650.30
0.40

366,865
3.06
4.42

366,865
0.26
0.36-0.027***(0.0052)-0.16***(0.023)-0.044***(0.0060)-0.069***(0.020)-0.011**(0.0050)

Panel A. DID
T reat × P ost

Post

Post

-1.65***(0.18)-0.36***(0.045)-1.77***(0.20)-0.41***(0.053)-1.45***(0.17)-0.30***(0.041)

Observations
Mean of Dep. Var.
SD of Dep. Var.

193,385
3.77
4.68

193,385
0.32
0.38

193,385
4.28
5.21

193,385
0.36
0.42

193,385
3.35
4.39

193,3850.28
0.36

Notes: Mean and SD of dependent variables reported are in levels. Dependent variables in the
regressions are in logarithmic scale. The matched sample for difference-in-differences estimationis generated by the kernel matching method. All columns are controlled for individual characteristics (age, sex, marital status, education level, household income, family size, and prefectureof residence) as well as month fixed effects. Standard errors shown in parentheses are clusteredat the individual level. *, **, and *** denote significance at 10%, 5%, and 1%, respectively.

5.2

Choice Adjustments

The average treatment effect described above does not reveal the timing of the shift inchoices nor the formation of a new purchasing pattern. Therefore, we conduct a seriesof event study analyses to explore the dynamics of changing consumer choices. As inequation (2), we normalize the timing of the stockout to period 0 and set the analysis
period to 5 months before and 15 months after the stockout. Figure 4 summarizes thepoint estimates and confidence intervals of the regressions.
We first examine product search behavior. While consumer choices are relatively
stable, they occasionally make taste discoveries. For example, if the consumer purchasesa product that is different from all of their previously purchased items, we would consider
this be be a new search. We propose that such discoveries are likely to be frequent
when some products are temporarily or permanently unavailable. Figure 4(a) showsthat product searching was prominent when JT’s entire product line was in short supply
16

immediately following the earthquake (t = −4). As the supply of products resumed, thenumber of searches plummeted and remained low for 15 months after the stockout.
In addition to the number of searches, we examine the tar levels of the products
searched to explore the composition of the consumer consideration set, which considers
all previously purchased items. This set is continuously updated as new options are
explored. We compare the realized choices of each month with the items in this updated
set to identify new searches. Variations in the tar levels of the consideration set are used
to determine the direction of a search. Given that multiple products are being searched,
we extract the average, upper, and lower bounds of the tar content in a cigarette to
examine their changes following the stockout. Figures 4(b)–4(d) demonstrate that the
new searches do not change the composition of the consideration set. The tar content of
newly searched items may be higher or lower than the existing items, making the change
in the average tar level of the consideration set negligible.
How likely are these newly searched products to enter consumers’ consideration andbecome regular choices? We first aggregate these newly searched products and thencompare them with the realized choices in the next period to verify that consumers
have selected them. As shown in Figure 4(e), the affected consumers were less likely tochoose newly searched products in periods following the stockout. In other words, these
consumers tend to choose familiar and frequently purchased products, even if the rangeof purchase options is narrowed.
Recall that affected consumers switched to low-tar products after the stockout. Yet
we have not explored when these consumers stabilized their new regular choices. We visualize changes in the most purchased products in Figure 4(f), which represent consumers’
typical or regular choices. Although consumers started product searches shortly after theearthquake, new regular choices did not form immediately, and in the first three months
following the earthquake, the direction of product searches varied across consumers. As
a result, the new typical choices did not stabilize until August 2011. Since then, affectedconsumers have continued to choose lower-tar products, with typical choices stabilizing
over time.
17

.2

1.5

−.2
−.4

Change relative to baseline

0

1
.5

Change relative to baseline

0

−4

−3

−2

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

−.6

−.5
−5

15

Month relative to the stockouts−5

Estimated coefficient

−4

−3

−2

0

95% CI

1

2

3

4

5

6

7

8

9

Estimated coefficient

11

12

13

14

15

(b) Upper (tar) bounds of consideration set

−.05
−.1

Change relative to baseline

.2
.1
−.1

−.15

0

Change relative to baseline

0

.3

.4

.05

(a) Number of newly searched products

10

95% CI

−5

−4

−3

−2

0

1

2

3

4

5

6

7

8

9

Estimated coefficient

10

11

12

13

14

15

−5

−3

−2

0

1

2

3

4

5

6

7

8

9

Estimated coefficient

10

11

12

13

14

15

95% CI(d) Average tar level of consideration set

15(e) Probability of choosing newly searched
products

0−.5

Estimated coefficient

15

14

14

13

13

12

12

11

11

10

95% CI

9

9

10

8

8

7

7

6

6

5

5

4

4

3

3

2

2

1

Estimated coefficient

1

0

0

−2

−2

−3

−3

−4

−4

−5

−5

−.4

−1

−.2

0

Change relative to baseline

.2

.4

.5

(c) Lower (tar) bounds of consideration set

Change relative to baseline−4

95% CI

95% CI(f) Most-purchased product type (tar)Figure 4: Event-study analysis of cigarette consumption
Notes: The dashed line represents the month in which stockouts occurred. The circles andspikes represent the estimated coefficients and 95% confidence intervals, respectively.

18

5.3

Cigarette Consumption

Next, we estimate the change in consumption using equation (1). The number of cigarettes
and the amount of tar (nicotine) purchased are used to measure consumption. The regressions are controlled for individual characteristics (i.e., age, sex, marital status, educationlevel, household income, family size, and prefecture of residence) and month fixed effects.
These results are reported in Table 3. Cigarette purchases presented in the first threecolumns are in levels, whereas the numbers presented in the last three columns are in
the logarithmic scale. In panel A, column 1 shows that affected consumers purchased
approximately 30 fewer cigarettes per month after the stockout of discontinued products,
a decrease of about 32 percentage points (as shown in column 4). Similarly, the total tar
and nicotine in purchased cigarettes reduced by about 278 mg (or 43 percentage points)
and 23 mg (or 30 percentage points), respectively. This reduction is equivalent to a
20-cigarette pack containing 14 mg of tar or 1 mg of nicotine per cigarette.
We further subdivide the study period into several segments and quantify the shortterm and long-term changes in purchases. The time intervals are categorized into periods
between the start of the analysis period and the 6th, 12th, 18th, and 24th months after
the stockout of discontinued products. Table A4 in the Appendix presents the results for
those intervals. In the 6th month following the stockout (February 2012), we observe that
affected consumers significantly cut their total purchases. They, on average, purchased
26 (or 26 percent) fewer cigarettes per month; this number continues to decrease if theanalysis period is extended. By the 24th month after the stockout, affected consumers
purchased an average of 32 (or 34 percent) fewer cigarettes per month. The total purchased tar and nicotine exhibit similar trends. In the first six months of the stockout,
the total tar and nicotine declined by 167 mg (or 34 percentage points) and 15 mg (or 23
percentage points), respectively. Further reductions in both indicators are observed if thestudy period is extended by another six months. As of the 24th month after the stockout,
the reductions in total tar and nicotine purchased reached 264 and 22 mg, respectively.

19

Table 3: Changes in the purchase volume
LevelsLogarithmic scales

Numberof cigarettes
(1)

Total
tar
(2)

Total
nicotine(3)

Numberof cigarettes
(4)

Total
tar
(5)

Total
nicotine(6)-30.1***(7.28)-275.0***(57.6)-22.5***(4.77)-0.32***(0.062)-0.43***(0.076)-0.30***(0.052)-288.7***(22.8)-1964.7***(226.8)-159.8***(18.2)-4.25***(0.21)-5.36***(0.27)-3.49***(0.18)

Observations
366,865
Mean of Dep. Var.
207.5
SD of Dep. Var.
305.9
Panel B. PSM-DID
T reat × P ost-30.4***(7.29)

366,8651316.02666.8366,865
109.0
214.1

366,865
2.99
2.86

366,865
3.76
3.62

366,865
2.46
2.48-278.3***(57.7)-22.7***(4.78)-0.32***(0.062)-0.43***(0.076)-0.30***(0.052)

Panel A. DID
T reat × P ost

Post

Post

-286.9***(34.3)-1721.6***(303.4)-142.6***(25.4)-4.08***(0.30)-5.06***(0.37)-3.27***(0.25)

Observations
Mean of Dep. Var.
SD of Dep. Var.

193,395
193.8
303.5

193,395
1200.22565.9193,395
99.8
206.8

193,395
2.76
2.87

193,395
3.45
3.62

193,395
2.26
2.47

Notes: All columns are controlled for individual characteristics (age, sex, marital status, educationlevel, household income, family size, and prefecture of residence) as well as month fixed effects.
Standard errors shown in parentheses are clustered at the individual level. *, **, and *** denotesignificance at 10%, 5%, and 1%, respectively.

Overall, product discontinuations have driven the affected consumers to shift products
and purchase fewer cigarettes. Such behavioral change is sustained in the long term. Our
results contrast with prior studies which find that the shift to low-tar (nicotine) cigarettes
can trigger compensatory behavior. In those studies, consumers purchase additional lowtar (nicotine) cigarettes to maintain the same level of nicotine demand as before (Evans
and Farrelly, 1998; Farrelly et al., 1999; Adda and Cornaglia, 2006; Cotti et al., 2016). The
compensatory behavior found in those studies was in the context of tax increases, where
consumers still had access to preferred products, although at a higher cost. However, suchcompensatory behavior does not exist in our case because of the complete restriction of
the consumer’s access to products. That is, the inaccessibility of products leads consumers
to focus on familiar products with less tar (nicotine) and reduce cigarette consumption.
20

6Discussion

Several factors that may contribute to the continued declines in cigarette purchases.

6.1

Product Relevance

As previously mentioned, consumers have different preferences for each product theypurchase. Therefore, the disappearance of a previously purchased item might not affectpurchasing patterns. However, losing regularly purchased products may have a tangible
impact on purchases. We, therefore, re-define the treatment group by excluding consumers who occasionally purchase one or two packs of discontinued products. Under thisnew definition, the number of affected consumers drops to 314. The results in Panel A of
Table 4 show that affected consumers consistently switch to lower-tar (nicotine) cigarettes
after product discontinuation. The tar and nicotine levels in an average cigarette declined
by 9.4 and 2 percentage points, respectively. In addition, the number of cigarettes purchased per month decreased by 17 percentage points, and the total amount of tar andnicotine consumed fell by 27 and 19 percentage points, respectively.
Furthermore, we re-define the treatment group to consumers whose most purchased
product was discontinued. We note that some consumers purchased the same quantity ofmultiple products, indicating substitutability between the products. As an extreme approach, we further restrict the treated consumers to those whose most purchased productwas not available. Under these conditions, the number of affected consumers declines to158. Panel B of Table 4 shows that affected consumers consistently switched to “lighter”
products, where tar and nicotine levels in a cigarette were lowered by 7.5 and 1.8 percentage points, respectively. There was not a significant decrease in total consumption.
Combining the results in Section 5, we find that the unavailability of products triggers
a sustained shift in choices to low-tar (nicotine) products, regardless of how we define thetreatment group.

21

Table 4: Treatment group re-defined
Tar per cig. Nicotine per cig. # Cigarettes
(1)
(2)
(3)
A. Exclude occasional purchases
T reat × P ost-0.094***-0.020***-0.17*(0.029)(0.0072)(0.096)Post-2.04***-0.46***-6.67***(0.29)(0.072)(0.84)N (treated consumers)
314
314
314
Observations
189,821
189,821
189,821
Mean of Dep. Var.
3.66
0.31
271.9
SD of Dep. Var.
4.60
0.38
337.5
B. Most purchased product was discontinued
T reat × P ost-0.075*-0.018*0.0044(0.040)(0.0092)(0.13)
Post-1.99***-0.44***-6.30***(0.38)(0.094)(1.00)N (treated consumers)
158
158
158
Observations
176,263
176,263
176,263
Mean of Dep. Var.
3.33
0.28
247.2
SD of Dep. Var.
4.64
0.38
339.1

Total tar
(4)

Total nicotine(5)-0.27**(0.12)-8.36***(1.07)
314
189,8211632.52898.7-0.19**(0.079)-5.53***(0.76)
314
189,821
137.3
238.7

-0.095(0.15)-7.94***(1.33)
158
176,2631530.43039.7-0.066(0.099)-5.14***(0.93)
158
176,263
129.2
252.5

Notes: Mean and SD of dependent variables reported are in levels. Dependent variables in the regressions
are in logarithmic scale. The matched sample for difference-in-differences estimation is generated by
the kernel matching method. All columns are controlled for individual characteristics (age, sex, marital
status, education level, household income, family size, and prefecture of residence) as well as month fixed
effects. Standard errors shown in parentheses are clustered at the individual level. *, **, and *** denotesignificance at 10%, 5%, and 1%, respectively.

6.2

Purchase Intensity

The addictive nature of nicotine makes it difficult to quit smoking. Given the degree of
nicotine dependence, heavy or frequent smokers may not exhibit a strong inclination to
reduce consumption compared to light or occasional smokers.
We examine the behavioral change of different types of smokers using purchase volume
as a proxy for addiction. We classify smokers as heavy (frequent) and light (occasional)
smokers using a threshold of five cigarettes per day based on average pre-earthquake
purchases. Table 5 presents the estimates for the changes in purchases for both typesof smokers. The tar content of the products chosen by frequent and occasional smokers
has been reduced by 5 and 8 percentage points, respectively. When taking productswitching into account, the total amount of tar in cigarettes decreased by 20 and 24
percentage points in the two groups, respectively. The nicotine content in a cigarette and22the corresponding total amount showed similar downward trends.
We then evaluate the variations in consumption by including a dummy variable representing smoker types in equation (1) and the following estimate:

Yit = α0 + α1 P ostt + α3 F requenti + β1 T reati × P ostt + β2 P ostt × F requenti(3)+γT reati × P ostt × F requenti + Xit θ + ηi + λt + εit
The coefficient for the triple interaction term captures the difference in the impact of
product discontinuation on both types of cigarette purchases. The estimates in the last
two lines of Table 5 show that there were no significant differences in product choices andpurchase volume in the two groups after product discontinuation, although the degree of
declines varied.
Table 5: Frequent vs occasional purchasers

Frequent(n = 416)

Tar per cig. Nicotine per cig.
(1)
(2)
-0.063**-0.014**(0.028)(0.0069)# Cigarettes(3)-0.17**(0.084)

Total tar Total nicotine(4)(5)-0.24**-0.18**(0.10)(0.073)

Occasional(n = 248)-0.058*(0.032)-0.014*(0.0078)-0.085(0.094)-0.14(0.11)-0.082(0.073)

Diff. in Means-0.0082(0.042)-0.00050(0.010)-0.097(0.13)-0.11(0.16)-0.11(0.10)

Notes: All the dependent variables are in logarithmic scale. The matched sample fordifference-in-differences estimation is generated by the kernel matching method. All
columns are controlled for individual characteristics (age, sex, marital status, educationlevel, household income, family size, and prefecture of residence) as well as month fixed
effects. Standard errors shown in parentheses are clustered at the individual level. *, **,and *** denote significance at 10%, 5%, and 1%, respectively.

6.3

Socioeconomic Disparities

Smoking is associated with the socioeconomic status (SES) of adults, and it is particularly
prevalent among people of low SES. The disparities in smoking are largely explained by
the education gradient (Cutler and Glaeser, 2005). Highly educated individuals tend tosmoke less and quit more often (Bratti and Miranda, 2010). Hence, changes in cigarettepurchases can be attributable to well-educated consumers. Here, we test the heterogeneous effect of product discontinuation on purchases based on educational groups.
23Columns 1–3 in Table 6 show that consumers in all education groups shifted to lower-tar
and nicotine products, purchasing 26–33 percent fewer cigarettes, and consuming 37–56
percent less total tar, and 24–39 percent less total nicotine.
Table 6: Cigarette purchases by SES groups
Education groups

Income groups

Secondaryor lower(1)

Juniorcollege(2)

High
education(3)

Low
income(4)Middleincome(5)

Highincome(6)

Tar per cig.

-0.13***(0.033)-0.12***(0.043)-0.11***(0.034)-0.036(0.044)-0.16***(0.041)-0.10***(0.036)

Nicotine per cig.

-0.029***(0.0080)-0.023**(0.011)-0.027***(0.0084)-0.0079(0.011)-0.040***(0.0099)-0.026***(0.0090)# Cigarettes-0.33***(0.099)-0.46***(0.12)-0.26**(0.11)-0.19(0.13)-0.40***(0.12)-0.37***(0.12)

Total tar

-0.46***(0.12)-0.56***(0.15)-0.37***(0.13)-0.21(0.16)-0.57***(0.15)-0.46***(0.14)

Total nicotine-0.32***(0.086)-0.39***(0.10)-0.24***(0.087)-0.14(0.11)-0.41***(0.10)-0.31***(0.092)

Notes: All the dependent variables are in logarithmic scale. The difference-in-differences
estimation in this table uses a matched sample generated by the kernel matching method.
All columns are controlled for individual characteristics (age, sex, marital status, educationlevel, household income, family size, and prefecture of residence) as well as month fixed
effects. Standard errors shown in parentheses are clustered at the individual level. *, **,and *** denote significance at the 10%, 5%, and 1%, respectively.

In addition to education, income is another driver for smoking disparities. Lowincome households tend to reduce their cigarette consumption when experiencing financialpressure because most of their income is spent on necessities. To evaluate the role ofincome in cigarette purchases, we first investigate the data of the unemployed or those who
experienced wage reductions after the earthquake. The data show that over 95 percentof consumers had consistent levels of income during the survey periods. Consumers
who experienced income reductions constitute less than 0.25 percent of the sample (seeFigure B.6 in the Appendix). Thus, unemployment or reduced income because of theearthquake cannot be considered a sufficient reason for reduced cigarette purchases.
We then exclude consumers with fluctuating incomes and re-estimate the changes
24

in cigarette purchases across income groups. As presented in columns 4–6 of Table 6,
consumers in all income groups exhibit varying degrees of reduction in the number ofcigarettes or tar (nicotine) content. Remarkably, the middle-income group (4–6.99 million
yen) and the high-income group (7 million yen and above) exhibited substantial reductionsin the quantity purchased and the tar (nicotine) in cigarettes. Affected consumers in these
two groups purchased 40 percent fewer cigarettes, and the total amount of tar and nicotine
in purchased cigarettes declined by 46–57 percentage points.

6.4

Geographical Variations

Previous research shows that people in earthquake-stricken areas become more risktolerant and tend to engage in more risky behaviors than people in other areas (Hanaoka
et al., 2018). Therefore, we consider that smokers in the Tohoku region—the epicenterof the earthquake— might not have reduced their purchases compared to smokers in theother areas.
We first examine the geographic distribution of the affected consumers, which is nationwide. About one-third of them are in South Kanto, and 15 percent are in Kinki.
This is followed by Tokai, Kyushu, and Tohoku, each accounting for about 10 percent(see Figure B.7 in the Appendix). Hence, product discontinuation is unlikely to affectonly consumers in the Tohoku region.
We now test the behavioral change in different regions and compare geographicalvariations. Similar to Subsection 6.2, we include an triple interaction term T reati ×
P ostt × T ohokui in equation (1), where T ohokui is a dummy variable representing thesix prefectures in Tohoku (Akita, Aomori, Fukushima, Iwate, Miyagi, and Yamagata). In
addition, we exclude consumers who changed residential areas during each survey period
to eliminate the influence of relocation on purchases. These consumers account for 2
percent of the sample (328 out of 16,533). The estimates in column 3 of Table 7 suggestthat consumers in the Tohoku region did not reduce their purchases any more than thosein other regions. Thus, product discontinuation had an equal effect on affected consumers
nationwide.
25

We note that the South Kanto region, where cities like Tokyo and Yokohama arelocated, has a high proportion of affected consumers. Therefore, we further compare
consumers’ purchases in South Kanto with those of consumers in other regions. The
results in column 6 confirm that there are no regional differences exist with respect tothe effect of product discontinuation on consumer purchases.
Table 7: Geographical variations
Tohoku vs. other regions
TohokuDependent variable(1)
Tar per cig.
-0.100*(0.056)South Kanto vs. other regions

Other
Regions(2)-0.11***(0.022)Diff.
in Means(3)
0.018(0.062)

South
Kanto
(4)
-0.12***(0.039)

Other
Regions(5)-0.11***(0.025)Diff.
in Means(6)-0.0062(0.047)

Nicotine per cig.

-0.013(0.013)-0.026***(0.0056)0.014(0.014)-0.028*** -0.023***(0.0096)(0.0062)-0.0047(0.011)# Cigarettes-0.53**(0.21)-0.29***(0.067)-0.23(0.23)-0.28**(0.13)-0.33***(0.074)0.043(0.15)

Total tar

-0.58**(0.25)-0.40***(0.083)-0.16(0.27)-0.39**(0.15)-0.43***(0.092)0.037(0.18)

Total nicotine-0.36**(0.17)-0.28***(0.057)-0.063(0.18)-0.26**(0.10)-0.30***(0.063)0.035(0.12)

Notes: All columns are controlled for individual characteristics (age, sex, marital status,
education level, household income, family size, and prefecture of residence)as well as month
fixed effects. Standard errors shown in parentheses are clustered at the individual level. *,**, and *** denote significance at the 10%, 5%, and 1%, respectively.

7

Conclusion

Using a natural experiment, we exploit this opportunity to investigate the effect of product discontinuation on tobacco consumption. This study predicts that restricting productavailability can constrain addictive behavior in the long term. The removal of certain
products does not trigger compensatory behavior in consumers for lost choices to maintain their addiction level; instead, it leads smokers to choose “lighter” products from a
restricted set of regular choices. The new choices can stabilize after a brief period of
choice discovery and adjustment. As product preferences shifted, tobacco consumption
exhibited an equivalent decline, not only in terms of the number of cigarettes but also in
26

the total amount of tar (nicotine) purchased. Such behavioral change is evident regardlessof the consumer’s level of addiction, education, income, or area of residence.
When faced with a restriction in the supply of a familiar cigarette, finding alternatives
with similar attributes outside of a familiar product range can make decision-making difficult. Information overload can confuse consumers and may delay their choices or cause
them not to choose at all (Dhar, 1997; Chernev, 2003). The shift to low-tar (nicotine)
products and reduced cigarette consumption may be attributable to growing health concerns. Smokers often perceive such cigarettes as a safer alternative to high-tar, full-flavor
cigarettes; however, low-tar (nicotine) cigarettes are equally harmful to health (Cohen,
1996). Thus, instead of quitting immediately, smokers switch to “lighter” cigarettes tocope with nicotine cravings, believing that this will reduce health risks. Furthermore,
smokers may prefer to exclude “stronger” or tempting products as a commitment to
reducing tobacco consumption (Gul and Pesendorfer, 2004).
Our study, therefore, points to some avenues for future research. For example, themechanism of the observed sustained behavioral change is yet to be explored. In additinon, consumer preferences and choices under consideration are built upon historicalpurchases. Further investigation of consumers’ choice sets or consideration sets is needed
to explain the drivers behind consumers’ reactions to the loss of regular choices. Moreover, our estimates imply that consumers are likely to use product discontinuation as acommitment device to reduce cigarette consumption. Laboratory or field experiments
like those conducted by Toussaert (2018, 2019) are needed to evaluate consumers’ intent
to reduce smoking or their use of commitment devices.

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Appendix
A

TablesTable A1: Descriptive statics of cigarette purchasers
Full Sample

Subgroup Means
Affected
48.08
0.68
0.64
2.87

Diff.
in Means-4.39***-0.19***
0.04***
0.11***

0.41
0.25
0.34

0.39
0.2
0.41

0.02***
0.04***-0.07***

1
1
1
1
1

0.3
0.21
0.17
0.15
0.16

0.29
0.21
0.18
0.14
0.18

0.01***
0.00-0.01***
0.02***-0.01***

1
1
1

0.12
0.65
0.24

0.14
0.67
0.2

-0.02***-0.02***
0.04***

Variables
Age
Male
MarriedFamily size

Mean SD Min Max Unaffected
43.94 11.6 16
73
43.7
0.5
0.5
0
1
0.49
0.68 0.47
01
0.68
2.98 1.33
16
2.98

Education
Secondary school or lower
Junior college or equivalent
Higher education

0.41
0.24
0.35

0.49
0.43
0.48

0
0
0

1
1
1

Household income<4 million
4-5.49 million
5.5-6.99 million
7-8.99 million≥9 million

0.3
0.21
0.17
0.15
0.16

0.46
0.41
0.38
0.36
0.37

0
0
0
0
0

Occupation
Blue-collar
White-collar
Student/unemployed/others

0.12
0.65
0.24

0.32
0.48
0.42

0
0
0

Notes: The table uses a pooled sample of consumers from 2010 to 2014. *, **, and *** denotesignificance at the 10%, 5%, and 1%, respectively.

31

Table A2: Composition of cigarette purchases
Treated group
Comparison group

Key products
0.30[0.39]
0.34[0.45]

Other JT products
0.20[0.33]
0.21[0.38]

JT discontinued Non-JT products
0.24
0.25
[0.37][0.37]–
0.45–[0.47]

Notes: The table shows the percentage of key products, other JT products, discontinued products,
and non-JT products in total consumer purchases before the earthquake. Standard deviations areshown in brackets.

Table A3: Descriptive Statics of Cigarette Purchasers: by Years
2010
Age
Male
MarriedTreated
43.15
0.58
0.64

2011

Control Treated
45.48
43.68
0.71
0.48
0.61
0.68

2012

Control Treated
46.81
42.81
0.69
0.52
0.63
0.67

Control
48.08
0.71
0.63

Education
Secondary school or lower
Junior college or equivalent
Higher education

0.42
0.22
0.36

0.40
0.19
0.41

0.43
0.24
0.32

0.40
0.20
0.4

0.43
0.24
0.33

0.40
0.20
0.40

Household income<4 million
4-5.49 million
5.5-6.99 million
7-8.99 million≥9 million and higher

0.30
0.21
0.15
0.16
0.18

0.29
0.22
0.17
0.14
0.19

0.30
0.21
0.17
0.15
0.17

0.29
0.22
0.18
0.14
0.18

0.32
0.20
0.17
0.15
0.16

0.30
0.21
0.19
0.13
0.17

Occupation
Blue-collar
White-collar
Student/unemployed/others

0.13
0.65
0.22

0.13
0.68
0.20

0.11
0.66
0.23

0.13
0.68
0.19

0.13
0.65
0.22

0.15
0.68
0.16

Notes: The table reports the means of the variables by treatment and control groups.

32

Table A4: Short- and Long-term effectLevelsLogarithmic scales# Cigarettes

Total tar

Total nicotine# Cigarettes

Total tar

Total nicotine

(1)

(2)

(3)

(4)

(5)

(6)

A. Until the 6th month after stockouts (Feb 2012)
T reat × P ost
Post
Observations-25.9***-166.8***-14.5***-0.26***-0.34***-0.23***(6.02)(46.3)(3.80)(0.051)(0.064)(0.043)-86.2**-887.9**-69.5**-0.55*-0.68**-0.49**(39.4)(384.1)(30.9)(0.28)(0.32)(0.22)

74,29474,29474,29474,29474,29474,294

B. Until the 12th month after stockouts (Aug 2012)
T reat × P ost
Post
Observations-25.7***-207.9***-17.6***-0.27***-0.36***-0.25***(6.61)(51.7)(4.25)(0.053)(0.066)(0.045)-166.8***-1454.8***-115.5***-2.30***-2.94***-1.96***(30.2)(362.0)(28.6)(0.22)(0.30)(0.21)

109,810

109,810

109,810

109,810

109,810

109,810

C. Until the 18th month after stockouts (Feb 2013)
T reat × P ost
Post
Observations-27.3***-237.4***-20.0***-0.30***-0.41***-0.28***(6.84)(54.5)(4.48)(0.057)(0.071)(0.048)-329.9***-2535.7***-202.0***-4.10***-5.12***-3.43***(46.5)(546.4)(42.9)(0.43)(0.60)(0.41)

143,555

143,555

143,555

143,555

143,555

143,555

D. Until the 24th month after stockouts (Aug 2013)
T reat × P ost
Post
Observations-31.6***-264.3***-21.9***-0.34***-0.45***-0.31***(7.19)(57.3)(4.74)(0.060)(0.074)(0.051)-426.8***-2749.1***-222.3***-5.16***-6.32***-4.18***(66.6)(505.3)(41.0)(0.38)(0.50)(0.36)

175,570

175,570

175,570

175,570

175,570

175,570

Notes: The difference-in-differences estimation in this table uses a matched sample generated by the kernel matching method. All columns are controlled for individual characteristics (age, sex, marital status,
education level, household income, family size, and prefecture of residence) as well as month fixed effects.
Standard errors shown in parentheses are clustered at the individual level. *, **, and *** denote significanceat the 10%, 5%, and 1% levels, respectively.

33

Figures

0

10

20
30
Tar per cigarette (mg)

0

0

.5

Nicotine per cigarette (mg)1
1.5
2Price per pack (Japanese Yen)
200
400
600
800

2.5

1000

B

40

0

2000

2005

Tar per cigarette

2010

2015

2020

Nicotine per cigarette(c) Weighted average of tar (nicotine)

20
Tar per cigarette (mg)

30

40

5

Market share (%)
10
15

20

25

(b) Correlation between tar and nicotine

0

0.60
0.40
0.20
0.00
1995

Nicotine per cigarette (mg)

6
4
2
0

Tar per cigarette (mg)

8

10

0.80

(a) Tar content per cigarette and price per
pack

10

2008

2011
1mg

2014
8mg

6mg

2017
14mg

2020
3mg

(d) Market share by tar typeFigure B.1: Tobacco market in Japan
Notes: The price per pack in (a) represents the price after the consumption tax increaseof April 2014. Subfigures (b)–(d) were generated using data from the Tobacco Institute ofJapan (see https://www.tioj.or.jp/data/index.html). The price per pack is calculated
as the monthly sales revenue divided by the number of cigarettes sold.

34

Figure B.2: Tar distribution of JT products
Notes: The data come from a JT press release in May 2011. The figure shows 94 products of
22 brands, of which two products from a brand (Zerostyle) are excluded due to lack of tar
and nicotine information. See https://www.jt.com/media/news/2011/pdf/20110512_
10.pdf for details.

35

Figure B.3: Purchases of discontinued products over time

10
8
kdensity _pscore
4
6
2
0

0

2

kdensity _pscore
4
6

8

10

Notes: This figure shows the total monthly purchases of discontinued products. The firstzero purchase occurred in August 2011.

0

.1
.2
.3
.4
propensity scores BEFORE matchingtreated0.1.2
propensity scores AFTER matching.3

control

Figure B.4: Diagnostic test for propensity score matching

36

m
m
m

11
11
8

6

5

20

m
11 9
m
20 10
11
m
20 11
11
m
20 12
12
m
20 1
12
m
20 2
12
m
20 3
12
m
20 4
12
m
20 5
12
m
20 6
12
m
20 7
12
m
20 8
12
20 m9
12
m
20 10
12
m
11

11

20

20

20

4

m

11

3

11

20

20

m

11

20
−600

−400

−200

Total tar amount (mg)
0200

Estimated coefficient
95% CI
Estimated coefficient(a) Product choice

Estimated coefficient
95% CI(b) Number of cigarettes

95% CI(c) Total tar in purchased cigarettes

Figure B.5: Common trends in cigarette consumption using matched sample

Notes: The dashed line represents the month in which stockout occurred. The circles andspikes represent the estimated coefficients and 95% confidence intervals, respectively.

37

20 3
11
m
20 4
11
m
20 5
11
m
20 6
11
m
20 8
11
20 m9
11
m
20 10
11
m
20 11
11
m
20 12
12
m
20 1
12
m
20 2
12
m
20 3
12
m
20 4
12
m
20 5
12
m
20 6
12
m
20 7
12
m
20 8
12
20 m9
12
m
20 10
12
m
11

1m

20
1

20 3
11
m
20 4
11
m
20 5
11
m
20 6
11
m
20 8
11
20 m9
11
m
20 10
11
m
20 11
11
m
20 12
12
m
20 1
12
m
20 2
12
m
20 3
12
m
20 4
12
m
20 5
12
m
20 6
12
m
20 7
12
m
20 8
12
20 m9
12
m
20 10
12
m
11

1m

20
1

−60

−1

−40

−.5

−20

Cigarettes

0

Tar per cigarette (mg)

0

20

.5

98

97

94

20

percentage
40
60

80

100

95

5

4
1

2

2

1

1

0

0

2011

2012

2013

no change

2014

increasedecrease

Figure B.6: Distribution of changes in household income by year

Hokkaido
TohokuNorth Kanto
South Kanto
Hokuriku
Tokai
Kinki
Chugoku
Shikoku
Kyushu & Okinawa
0

10

20

30

Percentage

Figure B.7: Geographical distribution of affected consumers
Notes: The regions are displayed from north to south of Japan.

38