2. the 3rd and 7th highest risks among Thai men and women, respectively, measured by the
Disability Adjusted Life Years (DALYs) (Ministry of Public Health, 2007). Here, total
mortality from smoking related diseases was recorded as 41,002 people, whereas morbidity
was found to be 509,473 people (Tobacco Control Research and Knowledge Management
Center, 2008).
To reduce tobacco consumption, the Royal Thai Government has formulated various
tobacco control policies along the lines of the WHO Framework Convention Tobacco
Control (Tobacco Control Research and Knowledge Management Center, 2008; World
Health Organization, 2013). These include the creation of smoke free environments; re-
striction on advertising; sale and sponsorship; support for smoking cessation services;
health warnings and health education.
The negative health and economic consequences has drawn attention of the Thai
government to intensively employ excise taxes as tools to curb tobacco consumption. A
study of tobacco expenditures has two main policy objectives. First, taxes are a major
source of government revenue. In Thailand, excise tax on tobacco has a primary aim of
generating tax revenues for the Royal Thai Government (Visaruthvong, 2007). Second, the
health risk of cigarette smoking is a main focus of health policy.
In Thailand, tobacco taxes have provided a favorable outcome in raising tax revenues
because there has been a large increase in the ratio of tobacco taxes to retail prices
compared to other countries (Sarntisart, 2011; World Health Organization, 2010). Since
1992, raising tax rates on tobacco from 55% to 85% and public health policies were
intensively implemented with the aim of reducing smoking initiation by young adults
(Chaloupka and Laixuthai, 1996; Chantornvong and McCargo, 2001; Excise Department,
2007; Thailand Tobacco Monopoly, 2009). The excise tax on cigarettes was increased nine
times from 55% in 1992 to 80% in 2009, equivalent to about 60–70% of the retail price to
consumers (White and Ross, 2015). As a consequence, tax revenues increased by 241%
and tobacco smoking reduced by 12%.
Recently, there has been literature on demand for tobacco in Thailand, but little detailed
econometric analysis and use of demographic variables. Tungthangthum (1997) analyzed
demand for cigarettes in Thailand using annual time series data from 1983 to 1994. The
author used double log generalized least squares to estimate the short-run and long-run
price elasticities of demand under myopic and rational addiction approaches. Per capita
consumption of cigarettes was related to the weighted-average real price per pack of
cigarettes, real gross regional product, population aged 15 years and above, the number of
foreign cigarette smokers (as a proxy for legal sale of foreign cigarette) and a dummy
variable representing the legal sale of foreign cigarettes. As a result, price elasticities of
demand for cigarette were obtained of À0.73 in the short-run; and between À0.90 to
À1.065 in the long-run.
Sarntisart and Warr (1993) estimated price elasticities of demand for alcoholic bev-
erages and tobacco (in the same group) using the Thai household consumption expenditure
data for 1988. In 2003, Sarntisart et al. (2003) also studied demand for tobacco products in
Thailand. The latter research applied a Linear Expenditure System (LES) to the 2000
socio-economic survey. The overall price elasticity of demand for tobacco products was
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3. estimated to be À0.39. Poorer households in urban areas were found to be more responsive
to price changes than their rural counterparts. Also, younger households were found to be
sensitive to price changes.
Finally, Chandoevwit and Dahldy (2007) examined the price elasticity of tobacco in
Thailand. The Almost Ideal Demand System (AIDS) was applied to consumption ex-
penditure from the national income accounts from 1983 to 2002 (1998–1999 data were
omitted because of the non-normal consumption shares due to the 1997 economic crisis).
The price elasticity of demand for tobacco products was estimated to be À0.8.
The above papers contained some similarities and differences. First, all studies focused
on commercially manufactured cigarettes rather than other types of tobacco products.
Second, Sarntisart et al. (2003); Sarntisart and Warr (1993) and Chandoevwit and Dahldy
(2007) made use of a complete demand system, while Tungthangthum (1997) relied on a
single demand equation. Third, Tungthangthum (1997) and Chandoevwit and Dahldy
(2007) used time series data from the national income accounts, but Sarntisart et al. (2003)
and Sarntisart and Warr (1993) employed cross-sectional data from the survey conducted
by the Thai National Statistics Office. Fourth, although they adopted different types of data,
price elasticities of demand for tobacco were estimated to have a range of between 0 and
À1. Finally, Chandoevwit and Dahldy (2007); Sarntisart et al. (2003) and Sarntisart and
Warr (1993) obtained only short-run price elasticities, whereas Tungthangthum (1997)
computed both short- and long-run price elasticities.
Some weaknesses of these studies are as follows: Even though all studies were carried
out at the national level and derived logical signs of coefficients, the research suffered from
lack of inclusion of demographic variables (Chandoevwit and Dahldy, 2007; Sarntisart
et al., 2003; Tungthangthum, 1997). The analyses made use of only positive values on
household tobacco expenditures (Sarntisart et al., 2003; Sarntisart and Warr, 1993), that
raises a question of not randomly selected data. None of these studies employed alcohol
data as this variable has been found to be a quite crucial factor in estimating demand for
tobacco. Other studies in Italy, the UK and the USA reported a strong link between tobacco
and alcoholic consumption (Aristei and Pieroni, 2008; Fry and Pashardes, 1994; Goel and
Morey, 1995; Jones, 1989). Internationally, many studies have used micro cross-sectional
data to explain factors affecting decision to smoke and how much to spend on cigarettes
(Atkinson et al., 1984; Blaylock and Blisard, 1991; Jones, 1989).
The above literature provides the basis of econometric analysis on demand for tobacco
in Thailand. This paper uses the Heckman two-stage sample selection method and the
Extended Linear Expenditure System (ELES). Data are from the 2009 socio-economic
Survey of Thailand.
2. Theoretical Model
Observations with zero values on consumption of tobacco found in cross-sectional surveys
has drawn attention to the problem of using Ordinary Least Squares (OLS) for estimating
coefficients. If samples are randomly selected, then OLS is efficient (Hill et al., 2008). But,
if only positive consumption of tobacco is chosen from a cross-sectional survey and OLS is
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4. adopted, the results are inconsistent and biased because the positive consumption of to-
bacco is not randomly selected.
The Heckman model consists of (1) the selection equation determining whether the
variable of interest is observed or not observed and (2) the equation of interest. In the
selection equation, N observations represent the whole sample, but only n for the variable
of interest is observed (n < NÞ. The first equation or the explicit selection equation is
added to the population equation of interest
y ¼ xβ þ u, EðujxÞ ¼ 0,
s ¼ 1½zγ þ v ‚ 0Š,
¼ 0, otherwise,
where
y is tobacco expenditure;
x is a matrix of variables that affect tobacco expenditure in the equation of interest;
β is a vector of coefficients;
u and v are random error terms;
z is a matrix of exogenous variables in the selection equation;
s ¼ 1 if we observe y and zero otherwise.
In selection bias, variables that do not belong in the selection equation may appear to be
determinants of y in the equation of interest when regressions are fitted to selected samples
(Heckman, 1979). If the errors of these two equations (u, v) are correlated, a selectivity
problem will arise when y in the linear equation of interest is computed from z given s ¼ 1.
This is because the sample selection problem also causes s and v to be related, E(vjz, s) is
simply the inverse Mills ratio (IMR) when s ¼ 1 (Wooldridge, 2006).
To overcome this shortcoming, we need consistent estimators as follows
Eðyjz, s ¼ 1Þ ¼ xβ þ λðzγÞ,
where λ is the IMR. This is the expected value of y given z and observed y is equal to xβ
plus additional terms depending on the IMR (λ) evaluated at zγ.
The IMR is equal to
λi ¼ λðziγÞ for each i:
We can calculate the value of s conditional on z from the first step (Probit model) using
the entire sample. After obtaining γ, we then compute the value of IMR and include this
term as an extra explanatory variable in the second step. The IMR is calculated from the
Probit regression (Heckman, 1979), which represents the ratio of the standard normal
density function to the standard cumulative distribution function or the probability of
observing non-zero over the probability of observing zero.
IMRih ¼
’ðD1h,…, Dkh, PiÞ
ðD1h,…, Dkh, PjÞ
for Zih ¼ 1
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5. and
IMRih ¼
’ðD1h,…, Dkh, PiÞ
1 À ðD1h,…, Dkh, PjÞ
for Zih ¼ 0,
where Dkh are the k demographic characteristics of the h household and Zih is a zero–one
variable where Zih ¼ 1 if the household consumes tobacco and Zih ¼ 0 if the household
does not consume tobacco. Finally, the least squares estimation of the structural equation
will yield consistent estimators.
2.1. The ELES
The ELES has been developed from an intertemporal maximization of the Stone–Geary
utility function (Howe, 1977). The model expresses total consumption expenditure as a
linear function of prices and income. Consumption expenditure will be an optimal allo-
cation at the beginning of the consumer plan when the instantaneous utility function is
Klein–Rubin (Lluch, 1973). It is a generalization of the LES in which the independence of
income from the errors in the expenditure equation enables identification. The alternative
development of the ELES is to employ current income instead of permanent income, which
implies that saving is excluded from current income and marginal propensity to consume
will reflect the current income version (Howe, 1977). The ELES assumes a two-stage
budgeting decision processes (Lewis and Andrews, 1989). Individuals allocate income to
purchase subsistence expenditures in the first stage and then they will allocate the
remaining amount of income on discretionary expenditures. Consequently, the ELES can
yield the subsistence expenditures and marginal propensity to consume from broadly
defined goods, in our cases; tobacco and all other goods. Our analysis will also add the
IMR into the ELES model
piqi ¼ piai þ bÃ
i Y À
Xn
j¼1
pjaj
!
þ γiIMRi,
where piqi is monthly household expenditure on good i; piai is subsistence expenditures;
bÃ
i is the marginal propensity to consume for good i and Y is monthly income.
The formulas for elasticities are as follows:
ey ¼ bÃ
i Y=piqi for i ¼ 1, 2
where ey, bÃ
i and Y are the income elasticity, marginal propensity to consume of good i and
monthly income of household, respectively, and piqi are the price and quantity of good i.
ee ¼ bÃ
i =wi, for i ¼ 1, 2
where ee is the expenditure elasticity, bÃ
i is the marginal propensity to consume of good i
and wi is the average budget share of commodity i.
eii ¼ ð1 À bÃ
i Þ=ðpiai=piqiÞ À 1, for i ¼ 1, 2
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6. where eii, bÃ
i and piqi are, respectively, the price elasticity, the marginal propensity to
consume of good i and the price and quantity of good i and piai is the subsistence quantity
of good i.
3. Data
The data are drawn from the 2009 socio-economic survey of Thailand conducted by the
Thai National Statistics Office. The 2009 survey data includes data on income, expendi-
tures, liabilities, assets, structure of household members, household characteristics, im-
migration and remittance transfer and social welfare payments from government (National
Statistics Office, 2009). The survey adopts a stratified two-stage sampling. There are 76
provinces (76 constituted strata) and each stratum was divided into municipal areas and
non-municipal (two sub-stratums) areas. First, the primary Sample Units are selected
blocks from municipal areas and villages from non-municipal areas independently using
the probability proportional to the total number of households in the blocks or villages.
The Secondary Sampling Units were selected private households (non-institutional
households residing permanently in municipal areas and villages of all regions) as ultimate
sampling units from the total number of households from the blocks and villages. At this
stage, households from every block and village are listed to serve as the sampling frame
and they are rearranged by size of household (number of household members) and type of
socio-economic classes (the occupation that generated the highest income in the house-
hold). Finally, private sampled households were selected by using stratified sampling for
each type of local administration.
A total of 51,970 households across the nation were surveyed during the period of
January–December 2009 and 43,844 households are available for the analysis. Here, data
are primarily collected by face-to-face interviews. The response rate was 84%. The
household survey does not identify individual smokers but household expenditure on
tobacco. Since there were missing data from the 2009 household expenditure and income
survey, the incomplete information was deleted from observations for this study. The
number of final observations consists of 41,229 households, of which 12,649 households
reported spending on tobacco. Hence, there are large proportions of households reporting
zero consumption of tobacco — which means it is important to accommodate zero ex-
penditure in this analysis.
4. Variables
Most of the previous research in Thailand has not incorporated demographic variables.
Here, a survey of the literature on consumption of tobacco from international studies is
used to suggest factors influencing the decisions to consume and how much to spend on
tobacco. The following are socio-demographic factors that have been posited as deter-
mining the decisions to smoke and consumption levels.
Education has been found to have an impact on the participation and consumption levels
of tobacco. Zhao and Harris (2004) observed that Australians who attained less than year
12 had higher probabilities of smoking than those with year 12 or diploma degree. Su and
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7. Yen (2000) argued that there was a negative relationship between education and the
probability of consuming cigarettes in the US. Garcia and Labeaga (1996) in a Spanish
study noted that those with higher education levels were less likely to be smokers. Man-
rique and Jensen (2004) found that highly educated people had lower levels of tobacco
consumption in Spain. Yen (2005a) reported that education was negatively related to the
likelihood of tobacco consumption being observed among US men and women, but tended
to have an opposite effect on consumption levels of smokers. Blaylock and Blisard (1991)
also found that more educated women in the USA were less likely to start smoking, but
smoker’s education level did not affect the number of cigarettes smoked. Similarly, Aristei
and Pieroni (2008) reported that higher education decreased both the probability of
smoking and the level of expenditure in Italy. In our study, the highest education attained
by the Thai household heads is a dummy variable and is broken down into five different
levels; i.e., kindergarten and primary school levels; secondary school level; high school and
vocational level; college and undergraduate levels; postgraduate level and other specialized
courses.
Occupation has been identified as one of the key determinants of the likelihood of
tobacco expenditures being observed and of consumption levels. Blaylock and Blisard
(1991) found that US working women had a lower probability of starting smoking and
Blaylock and Blisard (1992) obtained a lower probability of being smokers for low-income
women in the USA. On the other hand, Garcia and Labeaga (1996) provided no empirical
evidence of a relation with occupation in Spain. Yen (2005a) reported for the USA, both
white collar and employed men and women had a higher cigarette consumption than the
unemployed. Atkinson et al. (1984) argued that British white collar workers had reduced
their tobacco expenditures over time. Manrique and Jensen (2004) confirmed a negative
relationship between employed households and tobacco spending in Spain. Aristei and
Pieroni (2008) found that a white collar worker reduced both the probability of being
smokers and the consumption levels. From our viewpoint, the socio-economic class of
household heads is a dummy variable and is separated into eight groups; i.e., farmers that
own land; farmers that rent land; fishing; entrepreneurs; professional; laborers; other
employees and economically inactive.
Area of residence has been found to influence the probability of a household’s de-
cision to consume tobacco. Two studies have found that US men and women in urban
and suburban residences were less likely to consume tobacco compared to rural resi-
dences (Su and Yen, 2000; Yen, 2005a). These studies were different to the findings of
Blaylock and Blisard (1991, 1992). Another study in Spain indicated a positive impact
of households in rural regions on tobacco expenditures (Manrique and Jensen, 2004).
Our study also divides the area of residence into urban and rural areas, as a dummy
variable.
Size of household has been found to be positively related to tobacco purchase in the
Spain (Manrique and Jensen, 2004). Moreover, this factor affected both the probability of
consuming tobacco and the amount consumed (Garcia and Labeaga, 1996). The number of
family members is included as a continuous variable in our analysis.
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8. Gender tends to affect the preferences and behavior that influence participation and
consumption patterns. Males have been found to have greater participation in consumption
of tobacco than females in Australia (Zhao and Harris, 2004) and in the USA (Su and Yen,
2000). Atkinson et al. (1984) and Deaton and Irish (1984) emphasized that British men
were more likely to consume tobacco. However, Aristei and Pieroni (2008) concluded that
being male had a negative effect on both the probability of smoking and the number of
cigarette smoked in Italy. Yen (2005a) suggested that gender affected both the likelihood of
consuming tobacco and expenditure on tobacco in the US. Gender of household head is
included in our modeling as a dummy variable.
A number of analyses have shown different tobacco consumption prevalence according
to the age of household head. Zhao and Harris (2004); Su and Yen (2000) and Garcia and
Labeaga (1996) found that older people had lower probability of tobacco participation in
Australia, the USA and Spain, respectively. Likewise, Deaton and Irish (1984) found that
British household heads aged 45–60 had greater propensity to purchase tobacco than the
age group under 25, but this diminished for the age group 60 and over. Atkinson et al.
(1984) found that British youths aged above 16 with household members who smoked,
tended to consume more, but older people consumed less cigarettes. Manrique and Jensen
(2004) found the same results to Atkinson et al. (1984) for Spanish youths. Blaylock and
Blisard (1992) pointed out the probability of observing a smoker peaked at age 31, while
cigarette consumption peaked at age 40 and then declined. According to Yen (2005a), the
probability of consuming tobacco fell, but expenditure on tobacco in the USA rose when
the age of household head increased. Therefore, age of household head is also included as a
continuous variable in the study.
Finally, monthly household income has been found to play a key role in determining the
likelihood of tobacco being consumed (Garcia and Labeaga, 1996; Jones, 1989; Manrique
and Jensen, 2004). Aristei and Pieroni (2008) and Deaton and Irish (1984) indicated
tobacco consumption rose as household income rose, but at a decreasing rate. However,
Yen (2005a) found that income was not a significant factor in the consumption of cigarettes
for men and women in the USA and this agreed with Blaylock and Blisard (1992) for the
low-income women in the USA. Our study makes use of monthly household income as a
continuous variable.
The first step in our estimation in this paper is to model the decision to smoke. Our
study has followed a number of previous studies by employing demographic character-
istics; age of the household heads; household size, the presence of both children aged
below 15 and adults aged above 60 as well as region occupation types and education levels
and alcohol consumption as a binary variable to explain the likelihood of tobacco ex-
penditure being observed in the first step. Some explanatory variables from the first step
are included in the second step as well as the variables of most interest, price and income,
because they should have a role in determining the expenditure on tobacco.
Table 1 provides descriptive statistics from the 2009 socio-economic survey of Thai-
land. Average expenditure on tobacco was 197 Baht per month for the overall sample
compared to 216 Baht per month for consuming households. The mean of monthly income
from these selected households was around 17,005 Baht per month. Mean age of
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9. Table 1. Variables and Sample Statistics
Variables Definitions Mean Standard Deviation
Tobacco Monthly expenditure on tobacco
(Baht/Mth)
197 178
Consuming household (Baht/Mth) 216 175
Tobacco binary dependent
variable
1 ¼ tobacco expenditure being ob-
served, n ¼ 12,649 0 ¼ other-
wise
Alcoholic beverages Monthly expenditure on alcohol
(Baht/Mth)
249 533
Consuming household (Baht/Mth) 659 693
Alcohol binary dependent
variable
1 ¼ alcohol expenditure being ob-
served, n ¼ 6,521 0 ¼ otherwise
Continuous explanatory variables
Income Monthly income (Baht) 17,005 17,100
Age Age of household head (Years) 49 13
Household structure
Household size Number of household members 3.74 1.72
Presence of children aged
15
Presence of children aged below 15 0.54 0.50
Presence of adults aged
60
Presence of adults aged above 60 0.29 0.45
Binary explanatory variables Area of Residence
Municipal Municipal 0.48 0.50
Sex of Household Head
Male Male 0.84 0.37
Highest Education level of Household Head
Postgraduate and others* 0.07 0.26
Pre and primary Kindergarten and primary 0.55 0.50
Secondary Secondary school 0.20 0.40
High school and
vocational
High school and vocational 0.08 0.27
College and undergraduate College and university 0.08 0.28
Occupation
Farm operator that owns lands* 0.20 0.40
Farmers that rent lands Farm operator that rents lands 0.04 0.19
Fishing and forestry Fishing, forestry, hunting, agricul-
tural services
0.03 0.16
Entrepreneurs Entrepreneurs, trade, industry and
service
0.17 0.37
Professional Professional, technical and mana-
gerial
0.06 0.24
Laborers Laborers 0.09 0.28
Other employees Other employees 0.35 0.48
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10. household head was equal to 49 years and average household size was close to four people.
48% of the sample lived in urban areas and 84% of the household heads were male. In
terms of educational attainment, 55% of the heads of households graduated from primary
school or lower. With respect to occupation types, 35% of the overall respondents worked
as employees and 20% of the farm operator owns lands. Around 79% of the household
sample owned or rented houses. About 83% of the household heads were married or
married with unknown status, while 34% and 25% resided in Northeast and Central and
20% lived in South, respectively.
5. Results
5.1. The first step estimation
Table 2 reports both parameters and significance levels for the decision to consume to-
bacco. Household heads that drink alcoholic beverages are found to have a lower likeli-
hood of smoking tobacco — the two goods are substitutes. This tobacco parameter is
consistent with the studies of Jones (1989) in the UK, but contrary to Aristei and Pieroni
(2008) in Italy.
Table 1. (Continued)
Variables Definitions Mean Standard Deviation
Economically inactive Unemployment 0.06 0.24
Tenure
Own dwelling on land, dwelling on
rented land or dwelling on public
area*
0.79 0.41
Rent or rent paid by others Rent, rent paid by others or occu-
pied rented free
0.20 0.40
Hire Purchase Hire purchase or other 0.01 0.11
Marital status
Married or with unknown status* 0.83 0.38
Never Married Never married 0.05 0.22
Widowed Widowed 0.09 0.29
Divorced Divorced 0.02 0.12
Separated Separated 0.02 0.14
Region
South* 0.20 0.40
Bangkok Bangkok 0.04 0.20
Central Central 0.25 0.43
North North 0.17 0.39
Northeast Northeast 0.34 0.47
Note: *refers to the reference group.
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11. Table 2. Estimated Parameters for the Selection and Outcome Equations
(1) (2)
Variables Tobacco (Binary Dep Var) Tobacco Expenditure
Alcohol (Binary) À1.219***
LogAge 0.437*** À48.87***
Household size À0.113*** À10.85***
Children aged 15 0.136** À1.988
Adults aged 60 À0.00214 14.91***
Municipal 0.287***
Male À1.099*** À39.25***
Pre and primary À1.231***
Secondary À1.292***
High school and
vocational
À0.757***
College and
undergraduate
À0.341
Farmers that rent land 0.0943 4.419
Fishing and forestry À0.342*** 27.10**
Entrepreneurs 0.769*** 67.52***
Professional 0.939*** 102.8***
Laborers À0.403*** 23.68***
Other employees 0.0737 51.78***
Unemployed 0.753*** 36.67***
Rent or rent paid by
others
À0.186** 34.32***
Hire purchase 0.367 52.03***
Never married 0.487***
Widowed 0.0690
Divorced 0.121
Separated 0.114
Bangkok 0.814***
Central 0.462***
North 0.192**
Northeast 0.0676
LogIncome 99.65***
Alcohol expenditure 0.0101***
lambda À147.2***
Constant 2.458*** À505.2***
Observations 7,575 8,305
Rho À0.967 À0.967
Sigma 152.1 152.1
Lambda À147.2 À147.2
R-Squared 0.2511
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12. Heads of families with higher age have higher probabilities of consuming cigarettes
compared to younger household heads. This result is dissimilar to that of a number
of studies in the UK (Atkinson et al., 1984), for US women (Blaylock and Blisard,
1991), for the low-income women in the USA (Blaylock and Blisard, 1992), in Spain
(Garcia and Labeaga, 1996; Manrique and Jensen, 2004) and in Italy (Aristei and
Pieroni, 2008). Meanwhile, Zhao and Harris (2004) indicated that aging reduced the
chances of participating in tobacco and alcoholic beverages in Australia, but increased
the expenditure on smoking and drinking. Also, Yen (2005a) postulated older US men
had lower probability of smoking, but given that they started smoking, they smoked
more than younger men.
The results suggest that the larger the size of a household, the lower the likelihood of
consuming tobacco. Jones (1992) is the only study to report a similar result to our study.
On the other hand, our finding contradicts the studies by both Atkinson et al. (1984) and
Jones (1989) in the UK; Manrique and Jensen (2004) in Spain and Blaylock and Blisard
(1991) for women in the USA. The presence of children aged below 15 tends to increase
the probability of consuming tobacco. This finding is associated with Zhao and Harris
(2004), but not by both Blaylock and Blisard (1991) and Blaylock and Blisard (1992) in
the USA and Aristei and Pieroni (2008) in Italy.
Households residing in municipal areas have greater probability of participating in
cigarette smoking compared to those in non-municipal areas. This provides a similar result
to a number of studies in Spain (Manrique and Jensen, 2004), in the USA (Su and Yen,
2000) and for the low-income women in the US (Blaylock and Blisard, 1991, 1992).
Households with male heads are less likely to smoke cigarette relative to female household
heads, as it has also shown to be the case in Italy (Aristei and Pieroni, 2008). This is
opposite to the USA where men and women revealed lower likelihood of consuming
tobacco, but higher expenditure on tobacco (Yen, 2005b).
Household heads attaining pre and primary, secondary, high school and vocational
levels have a lower tendency to consume tobacco products compared to those with post-
graduate level qualifications. This result is similar to that found for Australia where people
with education levels less than year 12, diploma or degree resulted in lower probabilities of
smoking cigarettes (Zhao and Harris, 2004), but contrary to several studies in Spain, Italy
Table 2. (Continued)
(1) (2)
Variables Tobacco (Binary Dep Var) Tobacco Expenditure
Likelihood ratio 1241.61
Goodness of fit test
Correctly classified 90.78
Breusch–Pagan test for
heteroskedasticity 3.74
*** Statistically significant at the 0.01 level and ** at the 0.05 level.
Note: Standard errors are available from the authors upon request.
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13. and the US (Aristei and Pieroni, 2008; Lebeaga, 1999; Manrique and Jensen, 2004; Su and
Yen, 2000). Yen (2005a) suggested that education played a negative role in the probability
of smoking, but not the level of smoking among US males and females.
When examining household occupation, household heads who work in fishing and
forestry and laborers have lower tendencies to purchase tobacco than farmers that own
land. On the other hand, heads of the families who are employed in entrepreneurial or
professional occupations, or even unemployed, have higher probabilities of smoking. This
result is partly in agreement with a few studies. First, Garcia and Labeaga (1996) reported
that the unemployed people raised the probabilities of consuming cigarettes in Spain.
Second, US female workers had lower probabilities of consuming cigarette than non-
working women (Blaylock and Blisard, 1991, 1992). Third, Australian workers and un-
employed people tended to smoke, but students had lower probabilities of consuming
tobacco (Zhao and Harris, 2004). In contrast, our results are different to the studies in the
UK (Atkinson et al., 1984) and in Italy (Aristei and Pieroni, 2008).
Households that rent land demonstrate lower likelihood of smoking cigarettes, com-
paring to home owners. This evidence is consistent to the studies for the women in the
USA and for Australia (Blaylock and Blisard, 1991; Zhao and Harris, 2004), but different
from Manrique and Jensen (2004) in Spain and Aristei and Pieroni (2008) in Italy. With
respect to marital status, household heads who have never married are more likely to
consume tobacco than household heads who have married. Finally, in terms of region,
household heads living in Bangkok, Central and North exhibit a higher probability of
smoking than families that live in the South.
At the 5% significance level, the percentage of correct predictions of tobacco con-
sumption in this model is over 90%.
Our R-squared is around 25% and is fairly reasonable for studies using unit record data.
5.2. The second step estimation
As Table 2 shows, income has a positive impact on tobacco expenditure. Older household
heads tend to have lower consumption of cigarettes, although their probabilities of smoking
cigarettes being observed are greater. Households that have greater number of family
members have reduced expenditure on cigarettes. Having adults aged 60 or above at home
implies an increase in tobacco expenditure. Household with female heads, are more likely
to consume cigarettes given the decision to smoke in the first step.
Being a white collar employee, entrepreneur, professional, a blue collar, both laborers
and other employees; working in forestry and fishing and unemployed, have a greater
expenditure on tobacco than farmers that own land.
Households with hire purchase properties will also consume more cigarettes than home
owners.
Finally, although households who consume alcohol are less likely to smoke, for those
who do smoke, alcohol and cigarette consumption are positively related.
Lambda is statistically significant at the 1% level, indicating that failure to delete zero
observations will result in inconsistent and biased estimates.
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14. Table 3 reports parameter estimates for subsistence expenditure and marginal propen-
sity to consume. Thailand’s households have a subsistence level at 159 Baht and 3,856
Baht for tobacco and other goods, respectively. The parameters for the MPC for tobacco
and other goods are 0.004 and 0.52, respectively. The R-squared of tobacco are approxi-
mately 27%, while that of other good are around 63%. All coefficients also show statistical
significance at the 1% levels.
Average expenditure and marginal budget share are presented in Table 4. The average
expenditure for tobacco and other goods are 197 and 14,657 Baht per household. The
marginal budget share for tobacco is relatively low, 1%, compared to the marginal budget
share for other goods with 99%.
Income, expenditure and price elasticities are shown in Table 5. The income elasticities
for tobacco and other goods are equal to 0.34 and 0.63, respectively, suggesting that the
two goods are necessities. In general, households tend to purchase tobacco and other goods
less than proportionality with respect to a rise in income. The price elasticities for tobacco
products and other goods are estimated to be À0.27 and À0.87, respectively. These in-
dicate that tobacco is relatively inelastic demand, while for other goods, the elasticity is
close to unity. This low elasticity is consistent with the time series evidence for Thailand,
whereby prices of cigarettes have increased significantly overtime in Thailand with little
change in expenditure on tobacco. The estimated elasticity also falls within the range of
estimates for other countries. International studies have reported that the estimated price
elasticities of demand for tobacco is below À0.5 in several developing countries. These
include À0.21 in Turkey (Tansel, 1993), À0.18 in China (Mao and Xiang, 1997), À0.11 in
Brazil (Costa e Silva, 1998) and À0.13 and À0.18 for the short-run and long-run in
Malaysia (Al-Sadat, 2005).
The expenditure elasticities for tobacco and other goods are 0.29 and 0.53, respectively.
These imply an increase in total expenditure by 10%, will be associated with rising
expenditures on tobacco and other goods by 2.9 and 5.3%, respectively.
Table 4. Marginal Budget Shares and Average Expenditure
Average Expenditure (Baht/Month) Marginal Budget Share
Tobacco 197 0.01
Other Goods 14,657 0.99
Table 3. ELES Estimates
Subsistence (Baht/Month) MPC IMR R-Squared Observations
Tobacco 159*** 0.004*** À241.33*** 26.83 7,575
Other Goods 3,856*** 0.52*** À2131.22*** 63.40 7,575
***Statistically significant at the 0.01 level.
Note: Standard errors are available from the authors upon request.
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15. Our derived income elasticity for tobacco is higher than the estimated income elastic-
ities for low income classes in both urban and rural areas found by Sarntisart et al. (2003).
He obtained income elasticities of 0.17 and 0.15 for low income classes across urban and
rural areas, respectively.
The estimated own-price elasticity for tobacco is close to that found by Lluch et al.
(1977), but not for Sarntisart et al. (2003). However, it is not possible to compare directly
with the study by Lluch et al. (1977) because their estimated value contained a mixed
commodities between food, beverages and tobacco. Lastly, the estimated expenditure
elasticity is relatively high compared to Lluch et al. (1977), but low compared to Sarntisart
et al. (2003). Lluch et al. (1977) suggested the expenditure elasticity for food which is
equal to 0.84, while Sarntisart et al. (2003) estimated the expenditure elasticity for tobacco
to be 0.98.
6. Conclusion
This paper has used the Heckman method to estimate the factors affecting the choice of
smoking. Household demand elasticities for tobacco in Thailand were estimated using the
ELES. The results of the first step are as follow. Households that drink alcoholic beverages
will have a lower probability of smoking tobacco. Heads of families with higher age have
greater likelihood of consuming cigarettes. Households with larger size have a lower
probability of consuming tobacco. The presence of children aged below 15 years tends to
increase the probability of smoking cigarettes. Families residing in municipal areas have
higher likelihood of participating in smoking. Households with male heads have less
likelihood of smoking cigarettes.
Households with heads attaining from pre and primary education to high school and
vocational education, have lower tendencies to consume tobacco compared to those with
postgraduate levels of education. Fishing and forestry workers and laborers have lower
probability of consuming tobacco, while white collar workers, in particular, entrepreneurs
and professionals, as well as unemployed, appear to have higher propensities to smoke.
Tenants who rent land are less likely to consume tobacco compared to home owners. In
terms of marital status, household with heads who have never married have more proba-
bility of consuming tobacco than households whose heads married. Finally, families re-
siding in Bangkok, Central and North Thailand, exhibit higher likelihood of smoking
tobacco than those living in the South.
In the second step equation, income and the amount spent on alcoholic beverages are
likely to explain tobacco purchase. Older heads of the households tend to have lower
Table 5. Estimated Elasticities for the Thai Household Demand
Income Elasticity Expenditure Elasticity Price Elasticity
Tobacco 0.34 0.29 À0.27
Other Goods 0.63 0.53 À0.87
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16. cigarette consumption. The larger the size of a household, the lower consumption of
tobacco. However, the presence of adults aged over 60 have contributed to higher tobacco
consumption. Households with heads who are female, have greater tobacco consumption.
Occupations farmers, white and blue collars, including unemployed, have positive impacts
on tobacco expenditure. Tenants who rent land appear to consume more tobacco compared
to home owners. Lambda is significant, so deleting zero observations will result in in-
consistent and biased estimates.
Estimated demand elasticities demonstrate that tobacco and all other goods are neces-
sities. All expenditure elasticities also exhibit positive values and are less than one. Own-
price elasticities are À0.27 and À0.87 for tobacco and all other goods, respectively, which
suggest that tobacco is not as sensitive to price changes, as all other goods.
These results suggest several policy implications: For public health policies, using
excise taxes to reduce tobacco consumption is unlikely to be very effective. From a revenue
policy perspective, a low price elasticity of demand for tobacco means that tobacco taxes
can be considered a stable source of revenues for the Royal Thai Government.
This study contains limitations as follows. First, there is a lack of price data in the
household survey. Only expenditure data is available. Second, there are no data on non-
cigarette (legal or illegal) consumption such as on roll-your own. Finally, there is no
accounting in the modeling for the possible impacts of anti-smoking policy variables.
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