Preventive Medicine 134 (2020) 106042
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Preventive Medicine
journal homepage: www.elsevier.com/locate/ypmed
Price Elasticity of cigarette smoking demand in the Philippines after the
2012 Sin Tax Reform Act
T
Kent Jason Go Chenga,⁎, Miguel Antonio Garcia Estradab,c
a
Social Science Department, Maxwell School of Citizenship and Public Affairs, Syracuse University, 100 College Place, Lyman Hall Rm 309, Syracuse, NY, USA
School of Economics, University of the Philippines Diliman, Quezon City, 1101 Philippines
c
Congressional Policy and Budget Research Department, 3F Main Building, House of Representatives, Batasan Hills, Quezon City, 1126 Philippines
b
ARTICLE INFO
ABSTRACT
Keywords:
Cigarette
Price elasticity
Sin tax reform
Philippines
The Philippine tobacco excise tax reform law passed in 2012 drastically increased cigarette prices which were
historically low. A pack of 20 cigarettes costing nine cents (US Dollar) or less was taxed five cents in 2011. When
the reform took effect in 2013, each pack was taxed 24 cents which is almost five times the 2011 rate. Alongside
the increase in tax is a decline in the prevalence of tobacco use from 28.3% in 2009 to 23.8% in 2015. Seven
years since the reform took effect, policymakers are still debating whether the tax introduced was high enough to
significantly reduce smoking prevalence. This study estimated the total price elasticity of cigarette demand using
regression analyses on the pooled Philippine 2009 and 2015 Global Adult Tobacco Survey data with the excise
tax as an instrumental variable. Information from both tax regimes provided the variation in cigarette prices that
allowed for the estimation of the price elasticity of smoking participation and intensity. Age, sex, urban residence, educational attainment, employment status, wealth quintile, and media exposure were used as control
variables. Results confirm that cigarette demand is inelastic, given that total cigarette price elasticity of demand
ranges from −0.56 to −1.10 which means that for every 10% price increase, total cigarette demand declines by
5.6% to 11.0%. This study also provides total price elasticities for different subpopulations. Future studies can
use these elasticity estimates to forecast smoking prevalence and provide policy recommendations.
1. Introduction
Smoking is a serious public health concern. It is known to be the
leading cause of respiratory and cardiovascular diseases and cancer (US
CDC, 2017). The recent Global Burden of Disease (GBD) study estimates
that in 2015, one in four people smoke and one in 10 deaths is due to
smoking, adding up to 6.4 million deaths worldwide (Reitsma et al.,
2017). Smoking ranks second in the top causes of mortality, followed by
high systolic blood pressure (Forouzanfar et al., 2016; Reitsma et al.,
2017).
Tax increases are known to be the most effective intervention to
reduce tobacco demand, among other methods, such as postings of
health warnings, designation of non-smoking areas, banning of the
selling of tobacco products to minors, and ad restrictions (Jha and
Chaloupka, 2000; Jha et al., 2012; World Health Organization, 2015).
In this light, the Philippine government increased tobacco taxes
through Republic Act (RA) 10351 or the “Sin Tax Reform Act of 2012."
The law drastically increased tobacco prices which was historically low:
a stick of cigarette was as cheap as one Philippine Peso (PhP) or roughly
⁎
a few US cents back in 2012 (Kaiser et al., 2016). A pack of cigarettes
costing nine US cents (PhP 5 under a PhP 51 per USD 1 exchange rate)
was taxed five US cents (PhP 2.72) in 2011 but, because of the reform,
was taxed 24 cents (PhP 12) in 2013 or almost five times the 2011 rate.
The net retail price (NRP), which refers to the price of a pack of
cigarettes without tax and other charges (Estrada, 2018), is the basis for
the amount of excise tax imposed. There were four tiers prior to the
enactment of the reform, with each tier having a corresponding tax. The
reform decreased the NRP tiers from four to two, and eventually
abolished the tiers to create a uniform flat tax starting 2017. Beginning
2018, the unitary excise tax rate was supposed to increase by 4% annually. However, due to a tax reform law (RA 10963) passed in late
2017, a higher tax was imposed in 2018. Another law (RA 11346),
enacted in July 2019, changed the schedule of increases altogether and
raised the cigarette excise tax to a higher amount relative to the ones
provided under the two earlier laws. A portion of tax collections from
tobacco products is earmarked for public health programs and alternative livelihood projects for farmers in tobacco-growing regions.
Table 1 summarizes the sin tax law rates pre- and post-2012 reform.
Corresponding author.
E-mail addresses: kgcheng@syr.edu (K.J.G. Cheng), mgestrada@up.edu.ph, miguel.estrada@cpbrd.congress.gov.ph (M.A.G. Estrada).
https://doi.org/10.1016/j.ypmed.2020.106042
Received 14 July 2019; Received in revised form 26 December 2019; Accepted 22 February 2020
Available online 22 February 2020
0091-7435/ © 2020 Elsevier Inc. All rights reserved.
Preventive Medicine 134 (2020) 106042
K.J.G. Cheng and M.A.G. Estrada
Table 1
Sin tax rates in the pre-reform and reform periods.
Source: Based on tables from Estrada (2018)
Pre-reform Period
RA number (law)
Year
Tier 1 (NRP < PhP 5)
Tier 2 (PhP 5.00 ≤ NRP ≤ PhP 6.50)
Tier 3 (PhP 6.50 ≤ NRP ≤ PhP 10.00)
Tier 4 (NRP > PhP 10.00)
8420 of 1996
8424 of 1997
1997
1998
2000
2005
2007
2009
2011
PhP 1.00
1.00
1.12
2.00
2.23
2.47
2.72
5.00
5.00
5.60
6.35
6.74
7.14
7.56
8.00
8.00
8.96
10.35
10.88
11.43
12.00
12.00
12.00
13.44
25.00
26.06
27.16
28.30
9334 of 2004
Reform Period
RA number
Year
Tier 1 (NRP ≤ PhP 11.50)
Tier 2 (NRP > PhP 11.50)
10,351 of 2012
2013
2014
2015
2016
2017
2018 onwards
12.00
17.00
21.00
25.00
30.00
Annual increase of 4%
25.00
27.00
28.00
29.00
Notes: RA = Republic Act; PhP = Philippine Peso; NRP = Net Retail Price. NRP is the price of a pack of cigarettes without tax and other charges. Until 2016, tax
rates were based on NRP per pack. Higher-tiered cigarettes were taxed at higher rates. Tiers were decreased from four to two (2013-2016), and altogether abolished
to create a uniform excise tax rate from 2017 onwards.
developed by Cragg (1971) which remains to be widely used in this line
of research. Here, the decision to smoke (smoking participation) is
modelled separately from the number of sticks smoked (smoking intensity). The first model was estimated using Probit and Probit with an
Instrumental Variable (IV) while the second was estimated using Ordinary Least Squares (OLS) and the IV Two-Stage Least Squares (2SLS).
We ran the OLS and 2SLS for both sticks smoked and the log-transformed sticks smoked following many preceding studies (see Chaloupka
and Warner, 1999 and International Agency for Research on Cancer,
2011 for useful reviews, and the World Health Organization, 2010 for a
step-by-step guide in conducting economic analysis of cigarette demand
using GATS). Robust standard errors were used for all models to correct
for the possibility of heteroscedasticity. The two-part model can be
summarized by the equations below wherein α is the constant, β is the
coefficient of the main variable price, Χ is the matrix of other controls
that are mostly socio-economic variables, θ is the vector of coefficients
of Χ, A is a proxy for addiction and γ is its coefficient, and ε is the error
term.
Based on the Global Adult Tobacco Survey (GATS) (Department of
Health - Philippines and Philippine Statistics Authority, 2016), cigarette
smoking prevalence for individuals aged 15 years and older dropped
from 27.9% in 2009 to 22.5% in 2015. Among former smokers, 55.5%
pointed to high prices as the reason for quitting while 34.8% claimed
that anti-smoking policies made them quit. These findings may make it
seem that the excise tax reform is effective. Despite the 2012 reform,
data in 2016 show that the country was third in Southeast Asia (SEA),
with a smoking prevalence of 24.3%. This rate was 7.8 points higher
than Singapore which had the lowest percentage of smokers in the region (World Bank, 2018).
It should be noted that RA 11346, signed in July 2019, increases the
cigarette excise tax to 88 cents (PhP 45) by 2020 and eventually, to USD
1.18 (PhP 60) by 2023. This will be followed by an annual increase of
5% in the sin tax beginning 2024. RA 11346 was endorsed by the
President as an urgent measure and is seen to result in higher government revenues.
An updated estimate of the price elasticity of cigarette demand is
therefore warranted given future attempts to push for an even higher
excise tax. Previous elasticity estimates used household-level surveys
(Austria and Pagaduan, 2019; Quimbo et al., 2010) or macro-level data
(National Tobacco Control Team et al., 2008). To the best of our
knowledge, this study is the first to estimate the price elasticity of cigarette demand in the Philippines using 2009 and 2015 GATS data.
Having data from both pre-reform and reform periods provides the
variation in cigarette prices that allows for the estimation of the price
elasticity of demand. In addition, compared to household or macrolevel data, using individual-level data allows us to estimate the price
elasticity by smoking participation and smoking intensity and to control
for socio-demographic factors (International Agency for Research on
Cancer, 2011). The resulting price elasticity will help policymakers
decide what tax rate is needed to meet smoking prevalence targets and
how much tax revenues to expect under a proposed tax regime.
Part 1: Smoking participation =
+ price +
Part 2: Number of Sticks Smoked =
+ price +
+
+ A+
The smoking participation model results in the prevalence price
elasticity of demand while the smoking intensity model results in the
conditional price elasticity of demand. Summing these two elasticities
will result in the total price elasticity of demand.
To obtain the elasticities for different socioeconomic status groups,
we ran the same set of regressions on disaggregated data following
previous studies (e.g. Farrelly et al., 2001).
2.2. Data
GATS data was employed to estimate the price elasticity of cigarette
demand in the the excise tax reform period. The survey is the global
standard protocol for systematically monitoring adult tobacco use and
tracking key tobacco control indicators. It is a cross-sectional, nationally representative survey of Filipinos aged 15 and older, which used
multistage geographically clustered sample design. The 2009 and 2015
waves of the survey were pooled to allow the analysis to include both
pre-reform and Sin Tax Reform periods. The 2009 GATS had 9701
2. Methods
2.1. Statistical analyses
Cigarette demand was estimated using the two-part model
2
Preventive Medicine 134 (2020) 106042
K.J.G. Cheng and M.A.G. Estrada
respondents while the 2015 GATS had 11,644 respondents. Response
rates were impressive at 88.4% and 92.1%, respectively. Final study
sample included 9545 responses from 2009 and 11,489 responses from
2015 after dropping responses with missing values on variables of interest and outliers for the price variable.
Table 2
Computation of the instrumental variable used.
[A] No.
of sticks
per pack
2.2.1. Smoking participation and smoking intensity
Smoking participation was coded dichotomously, with a value of 1
for those who said they smoked daily and less than daily, and a value of
0 for those who said they did not smoke at all. On the other hand,
smoking intensity was measured in terms of number of sticks smoked
daily. For non-daily smokers, weekly number of sticks smoked was divided by seven.
2.2.2. Cigarette price
The survey asks the respondents to report the quantity and price of
their recent cigarette purchase. These were used to derive price per
stick.
In the regression, all left-hand side variables should be exogenous to
get unbiased estimates. However, self-reported price is not exogenous
since smokers get to decide which cigarettes to buy at a given price so
price is not out of their control. In addition, because non-current
smokers do not buy cigarettes, they do not have any price to report. But
it is incorrect to assume that the price they face is zero because they can
still choose to buy cigarettes, and their decision not to smoke is shaped
by price. To solve these two issues, the respondent gets assigned the
average price per stick of her/his primary sampling unit (PSU) or type
of residence (urban or rural) in case PSU information was not available.
Weighted tax per stick
Price per stick
[C] Tax per [D] Tax per
pack (PhP) stick
[C] ÷ [A]
[E] Weight
based on
volume
[B] ÷ Ʃ[B]
[F] Weighted
tax (per
stick)
[D] × [E]
2009
30
20
20
20
Total
35.52
2273.90
512.18
1265.23
4086.83
PhP 2.47
2.47
7.14
11.43
–
PhP 0.082
0.124
0.357
0.572
–
0.009
0.556
0.125
0.310
1.000
PhP 0.001
0.069
0.045
0.177
0.291
2015
20
20
10
20
Total
2916.58
1092.02
265.15
93.15
4366.9
21.00
28.00
21.00
28.00
–
1.050
1.400
2.100
1.400
–
0.668
0.250
0.061
0.021
1.000
0.701
0.350
0.128
0.030
1.209
a
Sourced from BIR annual reports (Bureau of Internal Revenue, 2009, 2015).
using principal components analysis (PCA) on responses to questions
that ask whether the respondent had own electricity, flush toilet, fixed
telephone, cell phone, television, radio, refrigerator, car, scooter,
washing machine, CD/VCD/DVD player, component/karaoke, laptop,
tractor, and motorized boat. The first principal components served as
the weights of the index. Set of measures selected were reliable, with a
Cronbach's alpha of 0.83 for 2009 and 0.81 for 2015.
Exposure to media was constructed using PCA on responses to
questions that ask whether the respondent had been exposed to messages about the harms of cigarettes from newspapers and magazines,
local television, radio, billboard, monorails, cinema advertisements,
health care facilities, malls, and warnings on cigarette packages. The
first principal components served as the weights of the index. Set of
measures selected were reliable, with a Cronbach's alpha of 0.80 for
2009 and 0.78 for 2015. Then, similar to cigarette price, each respondent was assigned the average media exposure index for her/his
PSU or type of residence (urban or rural), in case PSU information was
not available. This was done just in case endogeneity problems arise.
Resulting index was normalized to range from 0 to 1.
Following Tsai et al. (2005), addiction was controlled for using the
time to smoking the first cigarette after waking up which was categorized as within 5 min, within 30 min, within an hour, and after an
hour, with non-daily smokers as the reference group. This measure had
been found to be a good single-item measure of nicotine dependence
(Baker et al., 2007).
In preliminary analyses (results available upon request), the dichotomized year dummy (1 = 2015, 0 = 2009) was controlled for like
most pooled cross-sectional analyses. However, doing so led to multicollinearity issues as indicated by how sensitive the price coefficient
was when year was added. Specifically, price became large and positive
and lost statistical significance while year was positive and statistically
significant. In addition, there was strong correlation between price per
stick and the year dummy (correlation = 0.71). Because of these observations, we decided to drop the year variable in our final model.
2.2.3. Instrumental variable
Despite the effort described in the preceding subsection, the endogeneity problem with the price variable persisted (as shown in
Table 4). Therefore, following previous studies, the excise tax was used
as an instrument for price (e.g. Nargis et al., 2014; Odermatt and
Stutzer, 2018). As previously mentioned, the excise tax is levied based
on different levels of NRP which is the price of a pack of cigarettes
without tax and other charges. Unfortunately, the NRP and the corresponding excise tax cannot be determined from the respondent-reported price data. Therefore, an approximate percentage share of the
excise tax in the price per stick was used instead, computed as follows:
Approximate %of excise tax per stick =
[B] Volume
of removals
(in million
packs)a
1
wherein the weighted tax per stick is computed using the volume of
removals reported by the Philippine Bureau of Internal Revenue (BIR)
(Bureau of Internal Revenue, 2009, 2015). Table 2 shows how the
weighted tax per stick was computed:
The resulting weighted tax per stick for 2009 and 2015 is PhP 0.29
and PhP 1.21, respectively. Naturally, the tax should be lower than the
price per stick the consumer face. Thus, price-per-stick observations
that are equivalent or higher than the weighted tax per stick computed
were dropped (n2009 = 67, n2015 = 217).
2.2.4. Control variables
Following the literature, age during the time of interview, sex, type
of residence, education, employment status, wealth levels, exposure to
media relating to the dangers of smoking cigarettes, and addiction were
controlled for. Sex was coded as a dichotomous variable with 1 for
female and 0 for male. Type of residence was coded as a dichotomous
variable with 1 for urban and 0 for rural. Education level was categorized as elementary, high school, college, and higher than college,
with no formal education and less than elementary as reference.
Employment was simplified to employed, student or pupil, and not in
the labor force, with the unemployed as reference.
Following recent studies (e.g. Kostova et al., 2014; Rutstein and
Johnson, 2004; Sinha et al., 2012), a wealth index was constructed
3. Results
Table 3 shows the descriptive statistics. Smokers made up 23% and
28% of the total 2015 and 2009 samples, respectively. On average,
smokers from the 2015 sample smoked about 11 sticks per day while
smokers from the 2009 sample smoked about 10 sticks per day. Each
stick in 2015 cost around PhP 2.46, on average, while a stick in 2009
averaged PhP 1.08. Most of smokers were in their late thirties to early
forties, had high school education or less, were employed, and were in
3
Preventive Medicine 134 (2020) 106042
K.J.G. Cheng and M.A.G. Estrada
Table 3
Descriptive statistics.
Variables
2015
2009
All
Total
Sex
Male
Female
Education
No formal schooling to some elementary
Elementary graduate
Some high school to high school graduate
Some college to post college
Employment
Unemployed
Employed
Student
Not in the labor force
Wealth
1st Tertile
2nd Tertile
3rd Tertile
Residence
Urban
Rural
Addiction
Smokes within 5 min of waking
6 to 30 min
31 to 60 min
>60 min
Not daily smoker
Price
Sticks
Log of sticksa
Age
Media exposure index
Tax instrumental variablea
a
Smokers
All
Smokers
N
%
N
%
N
%
N
%
11,487
100.0
2746
23.9
9541
100.0
2738
28.7
5698
5789
49.6
50.4
2386
360
86.9
13.1
4675
4866
49.0
51.0
2278
460
83.2
16.8
2102
1539
4572
3274
18.3
13.4
39.8
28.5
687
437
1096
527
25.0
15.9
39.9
19.2
258
2948
3692
2643
2.7
30.9
38.7
27.7
107
1109
1013
509
3.9
40.5
37.0
18.6
462
7248
963
2814
4.0
63.1
8.4
24.5
127
2307
66
246
4.6
84.0
2.4
9.0
443
6192
702
2204
4.6
64.9
7.4
23.1
125
2264
69
279
4.6
82.7
2.5
10.2
4043
3618
3825
35.2
31.5
33.3
1205
838
703
43.9
30.5
25.6
3196
3168
3177
33.5
33.2
33.3
1123
980
635
41.0
35.8
23.2
4572
6915
39.8
60.2
1038
1708
37.8
62.2
4265
5276
44.7
55.3
1084
1654
39.6
60.4
Mean
2.46
SD
0.72
SD
0.61
16.78
0.16
11.08
15.6
30.2
13.8
22.4
18.0
SD
0.70
17.07
1.31
15.12
0.16
11.04
Mean
1.08
40.75
0.32
50.93
428
829
379
615
494
Mean
2.44
11.15
1.75
41.88
0.32
51.31
38.82
0.34
31.52
15.96
0.18
11.93
471
799
378
578
512
Mean
1.05
9.97
1.63
39.80
0.33
32.44
17.2
29.2
13.8
21.1
18.7
SD
0.57
13.45
1.38
14.84
0.18
11.97
N for these variables are smaller due to how these variables were constructed.
the lowest wealth quintile. It should also be noted that despite a law
that prohibits the sale of cigarettes to minors or those below 18 years of
age, individuals below this age remain to have access to cigarettes.
Table 4 shows the regression results. For the smoking participation
regressions (Columns 1–2), the test of exogeneity yielded a p-value
significant at the 1% level, leading to the rejection of the null hypothesis that all independent variables are exogenous. In addition, tax
is found to be a strong IV as the F-statistic of the reduced form regression (i.e. regressing the endogenous variable on the IV) is well
above the minimum threshold of 10 (Column 2). Therefore, the Probit
with IV is the appropriate modelling approach for smoking participation. Consistent with the literature, the model reveals that socio-economic factors indeed play a significant role in determining smoking
probabilities. The chance that a female may smoke is 36% lower than
males. Urban residents are 1% more likely to smoke compared to rural
residents. Having higher education greatly diminishes the chance of
smoking participation relative to those with no formal or less than
elementary schooling. Students are unlikelier to smoke by 18% versus
the unemployed. Being among the wealthiest is associated with an 8%
lower chance of smoking than the poorest. Counterintuitively, exposure
to health warnings contribute possibly to the chance of smoking,
probably because smokers are more likely to see health hazard warnings on a cigarette pack than a non-smoker. As expected, price is statistically significantly associated with lower smoking participation, and
the resulting prevalence price elasticity of demand is −1.24, meaning a
10% increase in price would lead to 12.4% reduction in the risk of
smoking participation.
The regression on sticks smoked (Column 3) did not have an
endogeneity issue per the test of exogeneity so the OLS estimator
(Column 4) will do. However, contrary to the conventions of the law of
demand, the coefficient for price is positive and its implied conditional
price elasticity is at 0.14 meaning a 10% increase in price would lead to
1.4% increase in sticks smoked among smokers. On the other hand, the
regression on log-transformed sticks smoked (Column 5) had an issue
with endogeneity and the coefficient of price is statistically insignificant
so the 2SLS with IV regression (Column 6) is more suitable. Its price
coefficient however is positive, yielding a conditional elasticity of 0.68,
meaning a 10% increase in price would lead to 6.8% increase in sticks
smoked among smokers. Both 0.68 and 0.14 seemed reasonable estimates of conditional price elasticity of demand, making total price
elasticity range from −0.56 to −1.10, meaning, a 10% increase in
price would lead cigarette demand to decrease by 5.6 to 11.0%.
Both the OLS of sticks smoked (Column 3) and the 2SLS IV regression of log of sticks (Column 6) yielded almost similar direction and
statistical significance levels of coefficients for the other socioeconomic
status variables, save for employment status which becomes statistically
significant and negative only in the 2SLS IV model (Column 6). Both
models show that sticks smoked increases by age but attenuates over
time. Being an elementary graduate, having some high school or
graduating from high school, or having college education or beyond are
positively associated with more sticks smoked relative to those with no
formal schooling to some elementary but wealth does not relate to
smoking intensity. Being female, not being unemployed, living in an
urban area, experiencing higher rates of media exposure, and not being
addicted (like a person who do not easily succumb to smoking within
5 min of waking up) are associated with less sticks smoked.
4
Preventive Medicine 134 (2020) 106042
K.J.G. Cheng and M.A.G. Estrada
Table 4
Regression results.
Independent variables
Price
Age
Age squared
Female (ref = male)
Education (ref = no formal schooling to some
elementary)
Elementary graduate
Some high school to high school graduate
Some college to post college
Employment (ref = unemployed)
Employed
Student
Not in the labor force
Wealth Tertile (ref = 1st Tertile)
2nd Tertile
3rd Tertile
Urban (ref = rural)
Media exposure index
Addiction (ref = smokes within 5 min of
waking)
6 to 30 min
Smoking participation
Number of sticks smoked per day
Log of number of sticks smoked per day
(1) Probit
(2) IV Probit
(3) OLS of no. of
sticks
(4) 2SLS with IV of no.
of sticks
(5) OLS of log of
sticks
(6) 2SLS with IV of log of
sticks
−0.0205***
(0.0034)
0.0066***
(0.0011)
−0.0001***
(0.0000)
−0.3570***
(0.0066)
−0.6675***
(0.0840)
0.0232***
(0.0034)
−0.0002***
(0.0000)
−1.0674***
(0.0699)
0.8358**
(0.3579)
0.3250***
(0.0707)
−0.0033***
(0.0008)
−2.6898***
(0.6583)
2.4751
(1.9979)
0.3130***
(0.0743)
−0.0032***
(0.0008)
−2.5046***
(0.7453)
0.0295
(0.0213)
0.0266***
(0.0056)
−0.0003***
(0.0001)
−0.3556***
(0.0527)
0.3870**
(0.1553)
0.0232***
(0.0061)
−0.0002***
(0.0001)
−0.3217***
(0.0583)
0.0045
(0.0109)
−0.0400***
(0.0104)
−0.1133***
(0.0118)
−0.4857***
(0.0808)
−0.3893***
(0.0470)
−0.5926***
(0.0414)
1.2993*
(0.7550)
1.6365**
(0.7544)
1.5745*
(0.8218)
2.8006
(1.8510)
2.4480**
(1.1223)
2.2842**
(1.1085)
−0.0023
(0.0555)
0.0558
(0.0526)
0.029
(0.0604)
0.3121**
(0.1448)
0.2212**
(0.0873)
0.1711**
(0.0872)
0.0141
(0.0143)
−0.1865***
(0.0203)
−0.0495***
(0.0160)
0.0386
(0.0470)
−0.4900***
(0.0770)
−0.1142**
(0.0538)
−0.6455
(0.9628)
−2.076
(2.0273)
−1.8293
(1.1409)
−0.619
(0.9774)
−2.0468
(2.0226)
−1.772
(1.1650)
−0.1481**
(0.0632)
−0.4034***
(0.1279)
−0.2305***
(0.0823)
−0.1430**
(0.0662)
−0.4030***
(0.1305)
−0.2067**
(0.0867)
−0.0358***
(0.0073)
−0.0758***
(0.0086)
0.0121*
(0.0067)
0.0644***
(0.0185)
−0.0648**
(0.0274)
−0.1327***
(0.0386)
0.1637***
(0.0273)
0.2392***
(0.0596)
−0.1201
(0.5055)
0.5228
(0.5857)
−1.2518***
(0.4682)
−2.1550**
(0.9223)
−0.1391
(0.5117)
0.2199
(0.6827)
−1.7502**
(0.7158)
−2.1873**
(0.9353)
0.011
(0.0373)
0.0538
(0.0425)
−0.0028
(0.0340)
−0.3580***
(0.0890)
0.0098
(0.0388)
−0.0093
(0.0510)
−0.1057*
(0.0565)
−0.3730***
(0.0936)
−4.0667***
(0.4461)
−5.2673***
(0.5063)
−7.7091***
(0.4471)
−5.8670***
(1.0332)
7.7293***
(2.2009)
5213
–
0.1381**a
0.60 [0.44]
31.21
−4.2034***
(0.4874)
−5.3244***
(0.5378)
−8.0032***
(0.5675)
−6.0609***
(1.1034)
4.5863
(4.3650)
5149
–
0.4124b
−0.3178***
(0.0392)
−0.4544***
(0.0464)
−0.7386***
(0.0415)
−2.2619***
(0.0636)
2.0577***
(0.1469)
5138
–
0.0515a
5.98 [0.01]
37
−0.3475***
(0.0434)
−0.4767***
(0.0499)
−0.8008***
(0.0500)
−2.3224***
(0.0680)
1.4044***
(0.3263)
5076
–
0.6808**b
31 to 60 min
>60 min
Not daily smoker
Constant
Observations
% correctly predicted
Elasticity
Test of exogeneity
First-stage F-test
21,028
76.77%
−0.0376***
29.16 [0.00]
147.77
20,752
76.62%
−1.2377***
Marginal effects at means are presented for the Probit model. % correctly predicted was computed using a threshold of 0.5. Standard errors are in parentheses.
***p < 0.01, **p < 0.05, *p < 0.1; p-values for the exogeneity test in [ ].
a
Computed by multiplying the coefficient of price with the ratio of average price and sticks smoked.
b
Computed by multiplying the coefficient of price with average price.
We carried out the same analyses on disaggregated data to arrive at
total price elasticities of demand for different subgroups of the population, shown in Table 5. Like our main results, model selection for the
elasticities depended on the endogeneity tests, the F-statistic of the first
stage regression, and the statistical significance level, sign, and magnitude of the price coefficient (in descending order according to importance).
every 10% price increase, total cigarette demand declines by 5.6% to
11.0%. Cigarette demand therefore is generally inelastic in the
Philippines. This study also provided total price elasticities for different
subpopulations.
This study's main finding are close to Kostova et al.'s (2014) pooled
analysis of all available GATS data in 13 low-to-middle income countries (LMICs) (including the Philippines) which applied the two-part
model. They found that price elasticity is −0.53, and similarly, better
socio-economic status is attributed to less probability of smoking participation. Moreover, higher wealth is associated with more sticks
smoked.
Results are quite similar with previous studies (International Agency
for Research on Cancer, 2011). For instance, price lessens the risk for
4. Discussion and conclusion
This study pooled the Philippine 2009 and 2015 Global Adult
Tobacco Survey data and estimated the total price elasticity of cigarette
demand to be in the range of −0.56 to −1.10, which means that for
5
K.J.G. Cheng and M.A.G. Estrada
Table 5
Price elasticity of cigarette demand per group.
6
Various groupings
A. Prevalence price elasticity
Na
Model used
B. Conditional price elasticity
Na
Model used
A + B = Total price elasticity
Female
Male
No formal schooling to some elementary
Elementary graduate
Some high school to high school graduate
Some college to post college
Unemployed
Employed
Student
Not in the labor force
1st wealth Tertile
2nd wealth Tertile
3rd wealth Tertile
Ages 15–19
Ages 20–24
Ages 25–59
Ages 60+
Urban
Rural
−1.5773
−0.9570⁎⁎⁎
−0.1107⁎⁎⁎
−0.0796⁎⁎⁎
−0.8679⁎⁎⁎
−1.3638⁎⁎⁎
Not significant
−0.9076⁎⁎⁎
−1.9326⁎⁎⁎
−1.8679⁎⁎⁎
−1.7857⁎⁎⁎
−1.3968⁎⁎⁎
−0.8380⁎⁎⁎
−1.9428⁎⁎⁎
−1.6799⁎⁎⁎
−1.0136⁎⁎⁎
−0.1063⁎⁎⁎
−0.7737⁎⁎⁎
−1.7784⁎⁎⁎
10,515
10,237
2365
4490
8159
5842
892 to 899
13,267
1641
4952
7115
6724
6913
1986
1829
13,530
2794
8737
12,015
IV Probit
IV Probit
Probit
Probit
IV Probit
IV Probit
1.1696
0.6285⁎⁎
−1.0437⁎
Not significant
0.1923⁎⁎
Not significant
Not significant
0.8259⁎⁎
1.7949⁎⁎
Not significant
0.1431⁎⁎
0.1592⁎
Not significant
Not significant
Not significant
0.6758⁎
0.2200⁎
0.5283⁎
0.2038⁎⁎⁎
627
4449
611
1417 to 1442
2085
1011 to 1028
241 to 246
4276
130
429 to 461
2123
1763
1314 to 1327
220 to 222
472 to 479
3726
490
2047
3130
2SLS IV of log of sticks
2SLS IV of log of sticks
2SLS IV of log of sticks
n/a
OLS of sticks
n/a
n/a
2SLS IV of log of sticks
OLS of sticks
n/a
OLS of sticks
OLS of sticks
n/a
n/a
n/a
2SLS IV of log of sticks
OLS of log of sticks
2SLS IV of log of sticks
OLS of sticks
−0.4077
−0.3285
−1.1544
−0.0796
−0.6756
−1.3638
n/a
−0.0817
−0.1377
−1.8679
−1.6426
−1.2376
−0.838
−1.9428
−1.6799
−0.3378
0.1137
−0.2454
−1.5746
⁎⁎⁎
IV Probit
IV Probit
IV Probit
IV Probit
IV Probit
IV Probit
IV Probit
IV Probit
IV Probit
Probit
IV Probit
IV Probit
⁎
p < 0.01.
p < 0.05.
p < 0.1.
N of those groupings with insignificant elasticities vary because some observations dropped when sticks were log-transformed or when the tax instrumental variable was included.
⁎⁎⁎
⁎⁎
⁎
a
Preventive Medicine 134 (2020) 106042
Preventive Medicine 134 (2020) 106042
K.J.G. Cheng and M.A.G. Estrada
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smoking participation more for females than males, but the increase for
cigarette demand as price goes up for female smokers is higher compared to male smokers. Price's effect on cigarette demand is most often
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Credit authorship contribution statement
Kent Jason Go Cheng: Conceptualization, Methodology,
Software, Validation, Data curation, Formal analysis, Writing original draft. Miguel Antonio Garcia Estrada: Conceptualization,
Validation, Data curation, Writing - review & editing.
Acknowledgments
The authors appreciate the nurturing environment their respective
institutions have provided that made this research possible. The authors
wish to acknowledge the valuable inputs provided by Dr. Stella Luz A.
Quimbo for a related research paper written by MAG Estrada.
Disclaimer
Views expressed and potential errors in this article are the authors'
alone, and not that of their affiliations.
Funding
This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.
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8