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RESEARCH ARTICLE
Inequality of the crowding-out effect of
tobacco expenditure in Colombia
[Juan Miguel Gallego
1
, Guillermo Paraje
1
, Paul Rodrı
´guez-LesmesID
2
*
1School of Economics, Universidad del Rosario, Calle, Bogota
´, Colombia, 2Escuela de Negocios,
Universidad Adolfo Iba
´ñez, Diagonal las Torres, Peñalole
´n, Santiago, Chile
*paul.rodriguez@urosario.edu.co
Abstract
In recent decades, policy initiatives involving increases in the tobacco tax have increased
pressure on budget allocations in poor households. In this study, we examine this issue in
the context of the expansion of the social welfare state that has taken place over the last two
decades in several emerging economies. This study explores the case of Colombia between
1997 and 2011. In this period, the budget share of the poorest expenditure quintile devoted
to tobacco products of smokers’ households doubled. We analyse the differences between
the poorest and richest quintiles concerning the changes in budget shares, fixing a reference
population over time to avoid demographic composition confounders. We find no evidence
of crowding-out of education or healthcare expenditures. This is likely to be the result of free
universal access to health insurance and basic education for the poor. For higher-income
households, tobacco crowds out expenditures on entertainment, leisure activities, and lux-
ury expenditures. This finding should reassure policymakers who are keen to impose
tobacco taxes as an element of their public health policy.
1. Introduction
The tobacco epidemic disproportionally affects low socioeconomic status (SES) households
[1–6]. Global efforts such as the World Health Organization Framework Convention on
Tobacco Control (WHO FCTC) have decades of promoting policies aimed at reducing smok-
ing prevalence, especially tax increases, which have proved to be effective [7–12]. Yet, evidence
shows that the demand for cigarettes is inelastic and households try to sustain their habits even
if they have to reallocate their expenditures [13–15]. For low SES households that have contin-
ued to smoke, an important concern is the implications of the greater fiscal burden that they
face as a result of tax modifications [11,16,17].
To compensate for the increase in prices, households need to decide where to draw
resources from, resulting in a reduced standard of living [18,19]. One option available for
households is to use income that they previously devoted to education and health expendi-
tures. There is evidence of this crowding-out effect that impacts human capital accumulation
(food intake, education, and health) and productive household investment in Bangladesh,
rural China, Costa Rica, Ghana, India, Indonesia, Kenya, Malawi, Montenegro, Pakistan,
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OPEN ACCESS
Citation: Gallego JM, Paraje G, Rodrı
´guez-Lesmes
P (2024) Inequality of the crowding-out effect of
tobacco expenditure in Colombia. PLoS ONE 19(5):
e0303328. https://doi.org/10.1371/journal.
pone.0303328
Editor: Enrique Teran, Universidad San Francisco
de Quito, ECUADOR
Received: October 11, 2023
Accepted: April 23, 2024
Published: May 21, 2024
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
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all of the content of peer review and author
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editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0303328
Copyright: ©2024 Gallego et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data is available at
DANE’s webpage. All codes required for
reproduction and final derived datasets are
available at: https://github.com/androdri1/tobacco_
ineq.
Serbia, South Africa, Turkey, Vietnam, and Zambia [20–34]. Yet, in middle-income countries,
an alternative is to use the resources that are saved by the expansion of social policy efforts.
These policies involve transfers, that can be in cash or in kind, that provide an additional
income to households that they might devote to the consumption of temptation goods. Evi-
dence on the magnitude of this reallocation from the evaluation of cash-transfers programs is
inconclusive [35].
In this paper, we explore the progression of the crowding-out effect of tobacco, along the
income distribution, in the context of growing tobacco prices and social security expansion.
We consider the case of Colombia, a middle-income Latin American country. We analyse
the changes in household budgets, across the income distribution, for smokers in comparison
with non-smokers with similar observed characteristics. We use a repeated cross-section of the
Colombian Quality of Life Survey (ECV, the acronym in Spanish) from 1997 to 2011. It
includes household expenditure data during a period that saw increasing tobacco prices
because of tighter tobacco control policies, and as a result, there was an increase in financial
pressure on the lowest SES households of smokers. Alongside, access to health insurance and
basic education increased notoriously in the study period and drastically reduced out-of-
pocket expenditures in those areas. We present an overview of these two characteristics below
in section 2.
To determine differences overtime on how budget-pressure of smoking affects the poorest
households, we undertake two empirical steps. First, to establish a comparable group of smok-
ing and non-smoking households each year, we use a genetic matching algorithm. Second, we
contrast budget shares differences over total expenditures quintiles, between smokers and
non-smokers. Alongside describing the dataset, section 3 presents the matching strategy of the
empirical step 1. It also presents the statistical model required to obtain the estimates described
in step 2. Results are presented in section 4, and section 5 presents the discussion and
conclusions.
2. Context
2.1 Tobacco control policies
In an attempt to curb the tobacco epidemic, Colombia implemented a diverse range of control
mechanisms that played an important role in the decrease in cigarette consumption. As part of
the adoption of the WHO FCTC, in 2009, an anti-tobacco law (Law 1335 of 2009) was intro-
duced that restricted smokers from consuming cigarettes in public areas. Then, in 2011, the
government implemented the marketing restrictions included in Law 1335 of 2009. In addi-
tion, several tax-based reforms were introduced between 1997 and 2011. Since 1995, several
low-powered tax increases have taken place involving specific contributions to sports, custom
tariffs, and other consumption taxes. A major reform took place in 2010 (Law 1393 of 2010)
when a unique tax was applied uniformly to both local and imported products, involving a
combination of a lump-sum tax and an excise tax. During the study period, there was an
increase of nearly 60% in the real average price per cigarette over the study period, as shown in
Fig 1.
The policy initiatives outlined above were associated with a substantial reduction in the
prevalence of smoking. In 1997, 25% of households reported consuming tobacco during the
previous week, while this figure was down to around 10% by 2011. Concerning SES differen-
tials, Panel A of Fig 2 shows prevalence by total expenditure quintiles from 1997 to 2011. In
the initial year, while prevalence is larger for the first quintile to the fifth. By 2011, there is
almost no difference across quintiles. This is in line with several studies that have found that
initial differences in smoking prevalence across different characteristics have narrowed over
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Funding: This project was funded under the GADC
project by the CIHR/IDRC [grant number 108442-
001], and Fulbright-Colciencias and Colombia
Cientifica – Alianza EFI 60185 contract FP44842-
220-2018, funded by The World Bank through the
Scientific Ecosystems, managed by the Colombian
Ministry of Science, Technology and Innovation
(MINCIENCIAS). Guillermo Paraje acknowledges
funding by ANID FONDECYT, 1201452.
Competing interests: The authors have declared
that no competing interests exist.
time [36–39]. Concerning expenditures, Panel B of Fig 2 presents a notorious SES gradient
observed for the budget share allocated to tobacco by smoking households: while it remained
the same for the richest quintile (less than 1%), it doubled for the poorest quintile, jumping
from 3.1% to 6.2%. These average budget allocations are similar to the range of average inter-
national allocations, which vary from 1% in Mexico and Hong Kong to 10% in Zimbabwe and
China [22].
2.2 Social policies
From 1997 to 2011 in Colombia, incomes grew and there was an important decline in poverty
levels, for instance, extreme poverty fell from 16.9% to 6.6% [40]. This is a period in which
Colombia, like other middle-income countries, introduced policies to reduce poverty and
inequality, which might compensate for the potential financial pressures of tobacco tax
increases. In particular, Colombia introduced a range of aggressive social policies aimed at
reducing poverty such as universal health insurance and basic education. One of the most rele-
vant improvements in the context of this study was the expansion of health insurance. Fig 3
shows health insurance coverage and self-reported health for smokers and non-smokers in
quintiles 1 and 5 in the sample selected by the analysis (see below). These figures reflect the dra-
matic improvement in access to health insurance [41]. In 1997, approximately 80% of people in
quintile 5 had insurance, but the figure was only 50% in quintile 1. In contrast, by 2011, nearly
90% of people had insurance regardless of their SES. As a result, Colombia has the second-
Fig 1. Tobacco price evolution in Colombia. Label: Authors’ calculations based on Consumer Price Index data.
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lowest out-of-pocket health expenditures in Latin America [42]. A similar scenario is observed
for education, and in our data, we observe a drastic reduction in both education and health
expenditures for all households along the income distribution, as shown in Panel C of Fig 2.
Fig 2. Smoking prevalence and tobacco budget share by total expenditures. Label: Authors’ calculations.
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The universal health insurance policy is reflected in people’s health status, whereby in 1997,
60% of respondents in quintile 1 reported that their health was bad, whereas by 2011 this figure
had fallen to 37%, while in quintile 5, the proportion was close to 25% in both 1997 and 2011.
These substantial improvements in quintile 1 are irrespective of smoking status, which might
be related to the fact that the insurance premiums and co-payments are tied to earnings and
not to specific risk variables. Besides, individuals are free to move between insurers, limiting
the ability of insurers to cream-skim according to risk.
Fig 3. Self-reported health status and affiliation with the health system. Label: Authors’ calculations.
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3. Methods
Below we present the analysis required for this study. First, we describe the data and the
matching strategy, and then the regression analysis. Ethical approval for this type of study is
not required by our institute given the use of secondary data.
3.1 Data and matching
To obtain tobacco consumption data that reflect changes through time, we used household
consumption data that were collected as a part of the ECV for 1997, 2003, 2008, and 2011.
These surveys include detailed household consumption records for the previous seven days.
Using this information, we constructed monthly equivalent household expenditures using
the OECD method. As described above, these data allow us to determine (i) whether there is
a tobacco user in the household (prevalence, based on expenditures) and (ii) the share of
budget expenditure on the following categories: tobacco, food, alcohol, clothing, household
services, health, education, transport, and other items. Online appendix A describes how
these categories were constructed. The shares are calculated based on total expenditure
including tobacco.
One important concern when comparing households of smokers across time is the compo-
sition differences: apart from time, the notorious reduction in smoking prevalence is not ran-
dom. Thus, different budget shares might be the result of different needs of the households.
Our goal with the matching is to replicate the characteristics of smokers of 2011 with those of
smokers and non-smokers from the previous years. For this, we implement a genetic matching
algorithm which has been used before for assessing crowding-out effects from tobacco [24].
The method uses genetic optimization to choose a group of Mcontrol units per treatment
unit, which are as closely as possible in a vector of characteristics [43]. The method chooses the
metric that is used to measure the distance between the vectors, where the objective is to mini-
mise the bias between treatment and the conformed comparison group (i.e. maximise the bal-
ance). Typically, the propensity score matching is added as an additional covariate. In our
implementation, we search for one smoker, and one non-smoker, per each 2011 smoker
household–treatment group—(M= 1), each year, in each expenditure quintile. The method
was implemented with the package Matching in R [44]. We consider as a robustness check a
kernel propensity score matching.
Table 1 compares non-smoker households and smoker households per year. For each vari-
able -all of them considered in the matching algorithm -, we observe the mean for both groups
per year. In the first row before matching and in the second after it. The asterisks reflect the
level of significance of a comparison of the means of smokers with non-smokers. The goal of
matching is to ensure similar distributions of the covariates, not only that the mean of each
covariate is the same.
Overall, in all years, we observe that relative to earlier cohorts, 2011 households that bought
cigarettes had household heads who were older, more likely to be female, more educated, and
lived in smaller households with fewer children. These trends also apply to the non-smoker
population. The matching strategy reduces those differences, but as differences are not
completely gone, the econometric model below includes these variables as controls. Therefore,
our study is based on the expenditure’s composition for smoker households similar to the ones
observed in 2011, which change over time.
Appendix Table B.2 in S1 File shows the balance after the kernel matching, which also
reduces substantially differences. Still, overall it is less successful than the genetic matching,
mostly coming from 2003 non-smoker population.
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3.2 Empirical strategy
Our goal is to determine differences overtime (1997 to 2011) on how budget-pressure of smok-
ing affects the poorest households, relative to the richest ones. To compare conditional means,
we use a linear model over a comparable sample of individuals. As exposed above, with our
matching strategy, we ensure that the observed characteristics, which determine household
expenditures, are comparable.
Here we compare, in a cross-section analysis, budget shares between smokers and non-
smokers. For each year, we estimate the regression
bðjÞ
i¼aðj;sÞ
0siþaðj;nsÞ
0nsiþX
5
l¼2½aðj;sÞ
lQðlÞ
it siþaðj;nsÞ
lQðlÞ
it nsi þ gðj;kÞXiþeðjÞ
ið1Þ
where s
i
and ns
i
are dummy variables indicating whether the household has a smoker. The vec-
tor X
i
represents the control variables, which are log-expenditures, squared log-expenditures,
log-age, female dummy, education level dummies, a dummy for living in an urban area, the
ratio of household members under 5 per adult, household size, and log-income. Then, the
parameter aðj;sÞ
lpresents, for smokers, the difference between budget-share for item jof house-
holds in quintile lrelative to quintile 1 (lowest quintile of the total expenditure adjusted for
household composition, ‘the poorest’). For non-smokers, the parameter aðj;nsÞ
ldoes the same.
Crowding-in/out for quintile 1 can be tested with the null aðj;sÞ
0¼aðj;nsÞ
0. Whether the smoking
status of the household is relevant for budget-share inequalities can tested with the null
aðj;sÞ
l¼aðj;nsÞ
l.
Table 1. Matching sample balance.
1997 2003 2008 2011
Variable Sample Smoker Non-Smoker Smoker Non-Smoker Smoker Non-Smoker Smoker Non-Smoker
Age NM 47.690*** 46.467*** 46.616*** 46.927*** 49.271 47.342*** 49.939 48.001***
M 49.241 48.363 48.613 47.267 49.946 49.094 48.705
Gender (Female = 1) NM 0.205*** 0.258 0.272 0.328*** 0.228*** 0.323*** 0.270 0.323***
M 0.229** 0.222 0.225 0.206 0.257 0.220 0.191
Primary school NM 0.842** 0.793 0.634*** 0.616*** 0.830** 0.752*** 0.811 0.700***
M 0.824 0.823 0.781 0.742 0.817 0.817 0.812
Secondary school NM 0.077 0.100 0.130*** 0.154*** 0.093 0.147*** 0.089 0.153***
M 0.090 0.079 0.077 0.078 0.083 0.077 0.067
Tertiary school NM 0.081*0.107 0.236*** 0.230*** 0.078*** 0.101 0.099 0.147***
M 0.086 0.098 0.142 0.180 0.101 0.106 0.121
Zone (Urban = 1) NM 0.540 0.609*** 0.773*** 0.814*** 0.537 0.604*** 0.539 0.584***
M 0.531 0.516 0.560 0.625 0.523 0.516 0.487
Ratio children-under-5/adults NM 0.801*** 0.782*** 0.580*** 0.637*** 0.590*** 0.685*** 0.515 0.652***
M 0.516 0.553 0.478** 0.510 0.501 0.500 0.492
Total individuals NM 4.612*** 4.106*** 3.922 3.665*** 4.226*** 3.978 3.919 3.876
M 3.750** 3.777 3.753 3.543 3.825 3.620 3.477
Notes: Per variable, the first row corresponds to the sample without matching (NM), and the second to the matched sample (M). Genetic matching with the propensity
score, with five neighbours, population size of the optimizer of 10000. Significance of t-test between smokers of each year, and smokers of 2011
*10%
** 5%
*** 1%.
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For each year, we estimate a Seemingly Unrelated Regression model (SUR), in which unob-
served terms eðjÞ
iare correlated across spending categories, since households simultaneously
decide the proportion of income spent in each good group and are constrained by a single
budget constraint.
4. Results
Rows A of Table 2 shows the average budget share for each expenditure category after match-
ing for smokers in expenditure quintile 1 (the poorest). Rows C do the same for non-smokers.
Some expenditure categories, such as transport, grew over time, while there were reductions in
expenditure on health, education, and clothing. This pattern is likely to be the result of sub-
stantial reductions in the costs of health and education services due to the roll-out of social pol-
icies. During the study period, full coverage was achieved in relation to health insurance and
basic education, mainly because of efforts in the public sector.
What we are interested in is the difference in trends between smokers and non-smokers
over time. Rows E in the table present the p-vale of a Wald test between the budget shares per
item is the same for smokers and non-smokers in the poorest quintile. Smokers’ households
tend to spend more on alcohol and less on transport and housing most years. There is no evi-
dence of crowding-out in health (negative but non-significant coefficients, but there is for edu-
cation in 1997 and 2011. For food, smokers’ households devoted fewer resources in 2008, it is
also negative for 1997 and 2011, but not significant at the 90% level.
Next, we consider how different households of the fifth quintile (the richest) with those of
the first (the poorest) in terms of budget shares. Rows B (smokers) and D (non-smokers) pres-
ent such differences. As usual, richer households devote a smaller proportion to food con-
sumption, and more to clothing and other expenditures. However, how different are those
gradients between smokers’ and non-smokers’ households? Rows F test how different are
those gradients. First, the alcohol crowding-in seems larger for richer households only in 2003.
Second, we observe that for richer households the observed smaller share of expenditures for
food of smokers’ households occurs in a smaller magnitude than for non-smokers; for “oth-
ers”, the extra share is small for smokers. Third, there are no significant differences across
smokers and non-smokers, for the gap in the shares for health and education between quintiles
5 and 1 (the richest to the poorest).
As a robustness check, we performed two exercises. First, the plain analysis without match-
ing (Appendix Table B.1 in S1 File). Second, the same analysis but using weights coming from
the kernel matching (Appendix Table B.3. in S1 File). The analysis without matching resulted
in lower standard errors, and as a result, in more rejected nulls. Still, magnitudes and direc-
tions are largely unchanged. In the case of kernel matching, results are closer to those of
genetic matching. Hence, we claim that the central messages are qualitatively the same.
5. Discussion
As shown in previous studies, financial pressure due to tax increases may affect human capital
accumulation [21–23]. The objective of this article is to determine if this is the case for low-
SES households, during a period when tobacco framework policies were introduced and at the
same time, publicly provided health and education services were expanded.
Crowding-out of human capital accumulation among low SES households might be a possi-
ble undesired effect of the tobacco control policies. Price increases might have induced a com-
positional change in smokers, as occasional consumers are more likely to cease consuming
tobacco in response to tax hikes than frequent smokers are. Thus, a larger percentage of house-
holds that continue to consume tobacco under a higher-price regime will be composed of
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Table 2. SUR Estimates for variation on shares between smokers and non-smokers.
Variable Alcohol Others
1997 2003 2008 2011 1997 2003 2008 2011
A: Q1 Smokers Share 0.012 0.009 0.008 0.016 0.087 0.071 0.091 0.092
(0.002) (0.001) (0.001) (0.002) (0.004) (0.004) (0.004) (0.003)
B: Q5 vs Q1 smokers share: QðlÞ
5sit 0.023 0.017 0.017 0.021 0.079 0.039 0.156 0.175
(0.006) (0.007) (0.006) (0.006) (0.018) (0.017) (0.019) (0.019)
C: Q1 Non-smokers Share 0.006 0.006 0.002 0.005 0.078 0.078 0.101 0.098
(0.002) (0.001) (0.001) (0.002) (0.004) (0.004) (0.004) (0.003)
D: Q5 vs Q1 non-smokers share: QðlÞ
5nsit 0.017 0.005 0.015 0.021 0.137 0.076 0.167 0.138
(0.006) (0.007) (0.005) (0.006) (0.019) (0.017) (0.020) (0.019)
E: Share difference smok. vs non-smok. Q1 0.006 0.003 0.006 0.012 0.007 -0.006 -0.013 -0.009
p-val 0.010 0.177 0.002 0.000 0.233 0.328 0.044 0.124
F: Gradient difference smok. vs non-smok. Q1 0.005 0.012 0.002 -0.000 -0.058 -0.037 -0.011 0.037
p-val 0.213 0.001 0.550 0.982 0.000 0.002 0.444 0.009
Variable Transport Housing
1997 2003 2008 2011 1997 2003 2008 2011
A: Q1 Smokers Share 0.031 0.041 0.039 0.054 0.160 0.252 0.293 0.203
(0.002) (0.003) (0.003) (0.002) (0.007) (0.006) (0.007) (0.006)
B: Q5 vs Q1 smokers share: QðlÞ
5sit 0.000 0.000 0.025 0.019 0.045 -0.015 0.067 -0.033
(0.007) (0.009) (0.008) (0.008) (0.020) (0.018) (0.018) (0.018)
C: Q1 Non-smokers Share 0.034 0.051 0.051 0.050 0.190 0.282 0.298 0.247
(0.002) (0.003) (0.003) (0.002) (0.007) (0.006) (0.007) (0.006)
D: Q5 vs Q1 non-smokers share: QðlÞ
5nsit -0.003 -0.013 0.016 0.027 0.011 -0.012 0.069 -0.060
(0.007) (0.009) (0.008) (0.008) (0.020) (0.017) (0.017) (0.018)
E: Share difference smok. vs non-smok. Q1 -0.002 -0.010 -0.013 0.005 -0.035 -0.028 0.008 -0.045
p-val 0.649 0.016 0.000 0.179 0.000 0.000 0.356 0.000
F: Gradient difference smok. vs non-smok. Q1 0.002 0.012 0.009 -0.008 0.034 -0.003 -0.002 0.027
p-val 0.647 0.042 0.074 0.161 0.016 0.824 0.852 0.059
Variable Food Clothing
1997 2003 2008 2011 1997 2003 2008 2011
A: Q1 Smokers Share 0.502 0.455 0.474 0.526 0.131 0.106 0.053 0.057
(0.008) (0.006) (0.008) (0.007) (0.009) (0.006) (0.004) (0.003)
B: Q5 vs Q1 smokers share: QðlÞ
5sit -0.099 -0.045 -0.294 -0.205 0.019 0.017 0.037 0.030
(0.019) (0.018) (0.022) (0.020) (0.021) (0.014) (0.012) (0.007)
C: Q1 Non-smokers Share 0.518 0.445 0.486 0.548 0.113 0.095 0.065 0.059
(0.008) (0.006) (0.008) (0.007) (0.009) (0.005) (0.004) (0.004)
D: Q5 vs Q1 non-smokers share: QðlÞ
5nsit -0.128 -0.071 -0.321 -0.209 0.023 0.025 0.025 0.025
(0.018) (0.018) (0.023) (0.021) (0.018) (0.013) (0.010) (0.009)
E: Share difference smok. vs non-smok. Q1 -0.015 0.008 -0.023 -0.018 0.016 0.009 -0.009 -0.002
p-val 0.134 0.379 0.049 0.118 0.212 0.316 0.138 0.721
F: Gradient difference smok. vs non-smok. Q1 0.029 0.025 0.027 0.004 -0.004 -0.008 0.012 0.005
p-val 0.018 0.052 0.062 0.792 0.748 0.401 0.072 0.471
Variable Health Education
1997 2003 2008 2011 1997 2003 2008 2011
A: Q1 Smokers Share 0.050 0.030 0.027 0.034 0.027 0.033 0.001 0.000
(0.003) (0.002) (0.003) (0.002) (0.003) (0.002) (0.001) (0.001)
B: Q5 vs Q1 smokers share: QðlÞ
5nsit -0.024 -0.004 0.008 0.010 0.013 -0.012 0.002 0.005
(Continued)
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frequent/heavy consumers [3–6,45]. As a result, the remaining smokers are less sensitive to
price and would be more likely to substitute other goods to maintain their habit [13–15]. This
crowding-out effect was found in several countries [20–23,32,46].
Our study complements the literature that explores the role of tobacco usage on household
expenditures. The main strength of this study is that we can explore crowding-out in a setting
of expansion of the welfare state. Most of the literature is based on the comparison of budget
shares between smokers and non-smokers in a static context [22]. While studies like Block and
Webb (2009), San & Chaloupka (2016), and Mugosa et al (2023) consider several years of
information, the treatment of information is still static [23,32,47]. Nyagwachi, Chelwa, and
van Walbeek (2020) use a dynamic setting for identification but still, their objective is to mea-
sure the amount of crowding out in a given moment [48]. We contribute by showing that
crowding out should not be a concern in middle-income countries once a strong welfare state
is in place.
In the Colombian case, between 1997 and 2011 there was a notorious increase in the budget
share allocated to smoking in comparable low-SES households of smokers and a reduction in
the budget share allocated to health and education. Therefore, while financial pressure on
smokers was growing via taxes, their disposable income was growing due to the income effect
of the social policies. The results presented above show almost no evidence of crowding-out in
health and food expenditures, on quintiles neither 1 nor 5. We also observe crowding-in of
alcohol most years, which is typically associated with tobacco consumption in the literature.
Such differences were not present for highest income households, aside from a larger crowd-
ing-in for alcohol. The only difference is that the extra share on clothing and other expendi-
tures of the highest quintile is smaller for smokers than for non-smokers. Therefore, the
growth in tobacco expenditures is mostly affecting the leisure, entertainment, and luxury
expenses of households.
Table 2. (Continued)
Variable Alcohol Others
1997 2003 2008 2011 1997 2003 2008 2011
(0.011) (0.008) (0.009) (0.009) (0.008) (0.008) (0.002) (0.002)
C: Q1 Non-smokers Share 0.054 0.035 0.033 0.034 0.037 0.032 0.001 0.002
(0.003) (0.002) (0.003) (0.002) (0.003) (0.002) (0.001) (0.001)
D: Q5 vs Q1 non-smokers share: QðlÞ
5nsit -0.003 -0.010 0.009 0.026 -0.011 -0.013 0.004 0.005
(0.012) (0.010) (0.010) (0.010) (0.008) (0.007) (0.002) (0.003)
E: Share difference smok. vs non-smok. Q1 -0.003 -0.006 -0.005 -0.002 -0.008 0.000 0.000 -0.002
p-val 0.466 0.137 0.317 0.705 0.026 0.969 0.746 0.047
F: Gradient difference smok. vs non-smok. Q1 -0.021 0.006 -0.001 -0.015 0.024 0.000 -0.002 -0.001
p-val 0.006 0.338 0.916 0.018 0.000 0.980 0.139 0.680
Notes: This table summarises the main results with total expenditure net of expenditure on tobacco. Estimates are produced after estimating a SUR on the sample
resulting from the genetic matching using ECV 1997, 2003, 2008, and 2011 data. Each set of columns corresponds to a category of spending per year. Quantiles are
based on total annual household expenditures adjusted for household composition (Hagennars et al., 1994). In each year, the unconditional shares for smokers and non-
smokers from quintile 1 are presented (rows A and C), as well as the difference of these shares for quintile 5 which correspond to Eq 1 estimated coefficients conditional
on controls (rows B and D). Below them, two tests compare the previous numbers between smokers and non-smokers (A—C, B—D), both of them computed with the
estimates of Eq 1. Controls include log-expenditures, squared log-expenditures, log-age, female dummy, education level dummies (primary or less [base], secondary,
tertiary), a dummy that indicates if the household resides in an urban area, the ratio of the number of children under 5 per adult, household size, and log annual-income
adjusted for household composition. Standard errors in parentheses.
https://doi.org/10.1371/journal.pone.0303328.t002
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Inequality of the crowding-out effect of tobacco
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It is important to mention that this study compares budget shares over time for a ‘specific’
group: those people who declared to be smokers and who had characteristics similar to those
of smokers in 2011. Hence, our estimates do not identify the causal effect of the expansion of
social welfare (on health and education) and/or tobacco control policies on the budget shares
of smokers. This limitation for the interpretation comes from the potential impacts of those
policies on the composition of smokers which our methodology cannot capture.
Finally, our study cannot isolate the effects of the social policy expansion from the changes
in tobacco control policies as they occurred simultaneously during the period. Still, as the
tobacco control policies were strengthening over time (stronger financial pressure on remain-
ing smokers), ours became a lower bound of the changes that would be derived solely from the
expansion of the benefits.
6. Conclusions
The tobacco control literature has shown that one of the implications of tobacco consumption
is that it results in less disposable income for households. As a result, household reallocate
their resources in a way that hampers human capital accumulation. Still, this argument implies
that increasing taxes would improve the situation of those quitting smoking but exacerbate the
problem of those who remain smoking. Hence, these policies could lead the ‘victims’ of
tobacco control, and their descendants, to poverty. We show that the Government can protect
them with effective general policies directed towards health and education, irrespective of the
smoking status of households.
The finding above is central for middle-income countries which would want to take the
appropriate measures of the WHO FCTC, but where parliaments and governments hesitate
based on the impact on their citizens. In the case of Colombia, tobacco prices are still some of
the lowest on the continent despite recent tax hikes in 2017, and further efforts are required
[12,49]. In general, countries might contain adverse effects on household budgets due to taxing
temptation goods when they expand their social security programs.
Supporting information
S1 File. The file “S1 File.pdf” includes.
i. Appendix A. Data
ii. Appendix B. Robustness.
(PDF)
Acknowledgments
We thank Geoff Whyte, MBA, from Edanz Group (www.edanzediting.com/ac) for editing a
draft of this manuscript. We acknowledge the valuable research assistance by Susana Ota
´lvaro.
Author Contributions
Conceptualization: Guillermo Paraje, Paul Rodrı
´guez-Lesmes.
Data curation: Paul Rodrı
´guez-Lesmes.
Formal analysis: Juan Miguel Gallego, Guillermo Paraje, Paul Rodrı
´guez-Lesmes.
Funding acquisition: Juan Miguel Gallego, Guillermo Paraje.
Investigation: Juan Miguel Gallego, Guillermo Paraje, Paul Rodrı
´guez-Lesmes.
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Inequality of the crowding-out effect of tobacco
PLOS ONE | https://doi.org/10.1371/journal.pone.0303328 May 21, 2024 11 / 14
Methodology: Juan Miguel Gallego, Guillermo Paraje, Paul Rodrı
´guez-Lesmes.
Project administration: Juan Miguel Gallego, Guillermo Paraje, Paul Rodrı
´guez-Lesmes.
Resources: Paul Rodrı
´guez-Lesmes.
Software: Paul Rodrı
´guez-Lesmes.
Supervision: Paul Rodrı
´guez-Lesmes.
Validation: Juan Miguel Gallego, Guillermo Paraje, Paul Rodrı
´guez-Lesmes.
Visualization: Paul Rodrı
´guez-Lesmes.
Writing – original draft: Juan Miguel Gallego, Guillermo Paraje, Paul Rodrı
´guez-Lesmes.
Writing – review & editing: Juan Miguel Gallego, Guillermo Paraje, Paul Rodrı
´guez-Lesmes.
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