PRICES, INFLATION, AND SMOKING ONSET: THE CASE OF ARGENTINA.
Guindon, G. Emmanuel ; Paraje, Guillermo R. ; Chavez, Ricardo 等
PRICES, INFLATION, AND SMOKING ONSET: THE CASE OF ARGENTINA.
I. INTRODUCTION
Tobacco use causes more than 6 million deaths per year worldwide, a
number that is expected to rise to more than 8 million by 2030. The
majority of these deaths will occur in low- and middle-income countries
(LMIC) where 80% of the deaths are expected to take place by 2030 (GBD
Risk Factors Collaborators 2016; Mathers and Loncar 2006). In addition
to the well-known long-term consequences of smoking, smoking during
childhood and adolescence causes serious contemporaneous health
problems. The US Surgeon General concluded in 2012 that there was
sufficient evidence to support a causal relationship between active
smoking during childhood and adolescence and reduced lung function,
impaired lung growth, and wheezing severe enough to be diagnosed as
asthma. There is also emerging evidence that youth smoking is associated
with various developmental and mental health disorders such as
schizophrenia, anxiety, and depression (US Department of Health and
Human Services 2012). Moreover, in addition to its considerable health
impact, tobacco use hinders sustainable development and has been
formally recognized as a priority for health interventions by the United
Nations General Assembly in its Sustainable Development Goals (de Beyer,
Lovelace, and Yurekli 2001; Collishaw 2010; United Nations 2015).
In Latin America, 13% of deaths among persons aged 35 years and
older can be attributed to tobacco use (IECS 2014). In Argentina,
tobacco smoking is responsible for more than 40,000 deaths every year
and almost 10% of all disability-adjusted life years lost (IHME 2013;
Pichon-Riviere et al. 2013). In Argentina, about 30% of men and 16% of
women were current smokers in 2012 (MSAL and INDEC 2013; World Health
Organization 2015). Argentina is one of the very few countries that has
not seen any appreciable decreases in cigarette consumption over the
past two decades. Compared to its neighbor Uruguay that has instituted
extensive tobacco control measures, Argentina fares particularly poorly
(Abascal et al. 2012).
A recent systematic review examined the impact of tobacco prices or
taxes on tobacco use in Latin American and Caribbean countries and found
that cigarette prices had a negative and statistically significant
effect on cigarette consumption (Guindon, Paraje, and Chaloupka 2015).
The review concluded that in most Latin American countries, total
own-price elasticity for cigarettes was likely below 1-0.51 but noted a
lack of studies that used individual-level data. The systematic review
identified two sets of studies that used aggregate time series data from
Argentina (Gonzales-Rozada and Rodriguez Iglesias 2013; Martinez, Mejia,
and Perez-Stable 2015). Estimates were remarkably similar across studies
and suggested a long-run price elasticity for cigarettes of about -0.3.
Studies that use aggregate time series data such as national cigarette
consumption or sales are unable to examine the effect that tax or price
changes have on smoking participation, consumption, onset or cessation,
or different responses across groups (e.g., across individuals of
different socioeconomic status [SES]). Another recent review examined
the impact of tobacco prices on smoking onset (i.e., the transition
between never smoking and smoking) and concluded that existing studies
did not provide strong evidence that tobacco prices or taxes affect
smoking onset, in part due to a reliance on empirical approaches that
were methodologically weak (Guindon 2014a, 2014b). Moreover, the review
identified only three studies that were conducted using data from LMIC
(China, Russia, and Vietnam) (Laxminarayan and Deolalikar 2004;
Arzhenovskiy 2006; Kenkel, Lillard, and Liu 2009). The review also
identified one study that used cross-country data from
non-representative national or sub-national surveys of school-children
and -teens conducted in 48 LMIC (Kostova 2013). More recently, the
impact of prices on smoking onset has been examined using data from
China, South Africa, and Vietnam (Guindon 2014a; Kostova, Husain, and
Chaloupka 2016; Vellios and van Walbeek 2016). On the whole, none of the
existing studies are easily generalizable to Latin American countries.
(1)
Argentina's situation presents a unique opportunity to examine
price responsiveness in a high-inflation environment. During most of its
recent history, Argentina experienced some of the world's most
acute inflation episodes. Between 1980 and 1991, overall prices
increased by more than 8,000,000,000%; in 1990 alone inflation neared
5,000%. This period of high inflation was followed by a rapid price
stabilization process, which saw inflation fall to 83% in 1991, 17.5% in
1992, and 7.4% in 1993. Prices have been relatively stable since the
early 1990s, but even so, overall prices increased by about 185%- 350%
between 2000 and 2011 (Cavallo 2013; INDEC 2014; Internationa] Monetary
Fund 2015). Economic and political actors have become so sensitive to
general price increases that in recent years the National Statistical
Office has been accused of deliberately reporting lower inflation
figures (International Monetary Fund 2013; The Economist 2012, 2014).
In market-based economies prices play an important informational
role; current prices are signals of future prices. Ball and Romer (2003)
argued that high inflation has an important twofold effect on the
relationship between prices and demand. First, inflation reduces the
informativeness of current prices, causing customers to make costly
mistakes about which long-term relationships to enter. Second, because
prices have become less informative, they have less influence on
consumers' decision, and as demand becomes less elastic, producers
increase their mark-ups. Ball and Romer argued that both effects can be
quantitatively important at moderate inflation rates. Between 1980 and
1991, inflation was anything but moderate in Argentina fluctuating
between 80% and 5,000% annually. Such high rates of inflation may lead
markets to break down completely. In the context of smoking, during high
inflation periods in which prices provide little information, prices may
cease to have any effects on tobacco use generally and smoking onset in
particular. Consequently, it may be the case that governments lose their
most potent policy tool to reduce tobacco use (i.e., taxes that increase
real tobacco prices) during periods of high inflation. High inflation,
however, may affect smoking decisions through other pathways than price.
For example, Deaton (1977) suggested that unanticipated inflation may
lead consumers to believe that all goods are more expensive and result
in a decrease in real consumption. Inflation may impact time preference,
which may in turn affect smoking (Gong 2006; Khwaja, Silverman, and
Sloan 2007). Or times of high inflation may lead to increased stress
which in turn may effect smoking onset (Finkelstein, Kubzansky, and
Goodman 2006; Iakunchykova et al. 2015).
Argentina is an important producer of tobacco leaf and one of the
very few countries not to have ratified WHO'S Framework Convention
on Tobacco Control (FCTC). The 2011 National Tobacco Law, which came
into effect in 2013, generally follows the recommendations of the FCTC
including a ban on smoking in all indoor areas. Provinces, however, have
unevenly implemented important provisions of the National Tobacco Law.
The general lack of leadership at the federal level has led many
provinces and some municipalities to introduce smokefree policies prior
to the National Tobacco Law. There is some evidence that smokefree
policies decreased acute coronary syndrome admissions after their
implementation in the province of Santa Fe and the city of Buenos Aires
and improved respiratory health in the city of Neuquen (Ferrante et al.
2012; Schoj et al. 2010). Although most tobacco advertising, promotion,
and sponsorship were banned in 2013, advertising at point of sale is
still pervasive. Prior to 2013, tobacco advertising, promotion, and
sponsorship were widespread. Advertising and promotion were loosely
regulated in 1986 by Law 23.344, which also mandated a warning label for
cigarettes. Argentina's two leading multinationals--Massalin
Particulares (Philip Morris) and Nobleza-Piccardo (British-American
Tobacco)--control nearly 100% of the Argentinian cigarette market (ERC
2014).
Argentina's overall tax system is extremely complex; the
structure of tobacco taxation, and in particular, the taxation of
cigarettes, even more so. In addition to general taxes such as
value-added tax that are broadly applied to most goods and services,
Argentina uses two taxes that are specifically applied to cigarettes.
First, an ad valorem tax (impuestos internos or internal tax) is applied
on the retail price of cigarettes (minus other taxes) at a rate of 75%,
last changed in May 2016 (the rate had been 60% since 1996). In 2004, a
minimum tax revenue condition was introduced which stipulates that the
revenue from the internal tax cannot be less than 75% of the tax levied
on the most popular brand. In January 1996, Argentina introduced an ad
valorem emergency tax (Impuesto Adicional de Emergencia [IAE]) at a rate
of 7% of the retail price, net of all taxes, later increased to 31% in
December 1999. The IAE was subsequently decreased to 16, 12 and finally
7% in February 2001. There is also a relatively small dedicated tax
(Fondo Especial del Tabaco) that is used to support tobacco growers.
Recently, nominal cigarette price increases have been set by agreement
between the Ministry of Finance and the tobacco industry to reach
particular tax collection objectives (Rodriguez-Iglesias et al. 2015),
though very little information is publicly available for the terms of
these agreements. Since the early 2000s and until very recently, on the
whole, taxes on cigarettes have either remained relatively stable or
have decreased. More importantly, cigarettes in Argentina have become
more affordable: both overall inflation and incomes have outpaced
increases in nominal cigarette prices since the early 2000s
(Rodriguez-Iglesias et al. 2015). Figure 1 presents a timeline of key
tobacco control measures and key political, monetary, and inflation
events.
The overall mixed evidence from high-income countries, the growing
but still limited evidence from LMIC, and the lack of evidence from
Latin American countries calls for additional analyses of the impact of
tobacco prices or taxes on smoking onset. Moreover, Argentina is a
particularly interesting case to examine in this regard since it is a
middle-income country with high smoking rates coupled with recent
periods of very high inflation. Our objective is to examine the
relationship between cigarette prices and smoking onset in Argentina
while giving special attention to overall price inflation, tobacco
control policies, and sex and socioeconomic differences in the effect of
prices on smoking onset.
II. DATA AND METHODS
A. Data
As a measure of nominal cigarette prices, we used the manufactured
tobacco component (of which cigarettes represent nearly 100%) of
Argentina's consumer price index (CPI) for Greater Buenos Aires
(indice de Precios al Consumo Gran Buenos Aires [IPC]) from the
Instituto Nacional de Estadistica y Censos (INDEC). This approach is not
problematic as individuals residing outside Greater Buenos Aires face
the same cigarette prices as those residing in that area (i.e.,
cigarette prices do not vary by neighborhood or regions in Argentina).
Other countries such as Chile and France have similar
"uniform" price regulations (i.e., prices vary between brands,
but the prices of individual brands do not vary across space). These
data were available from January 1980 to May 2008. From May 2008 to
2011, we used the after-tax monthly weighted average price for a pack of
20 cigarettes reported by the Ministerio de Agricultura, Ganaderia y
Pesca, as INDEC stopped reporting CPI for individual categories in May
2008. (2)
We adjusted all nominal prices for overall inflation. As a measure
of overall inflation, we used CPI all-items for Greater Buenos Aires
(IPC nivel general) which was available for the period 1980-2011. The
quality, or lack thereof, of INDEC's CPI figures from 2007 is well
documented (International Monetary Fund 2013; The Economist 2012, 2014).
In February 2013, after several warnings, the International Monetary
Fund issued a declaration of censure against Argentina. As an
alternative measure of inflation from January 2007, we used estimates
for the Sante Fe province (the second largest province, situated in the
central region) calculated and reported by the Sante Fe statistical
office. As a sensitivity check, we used estimated calculated by
MIT's Billion Prices Project (Cavallo 2013) and State Street, a
financial services firm. The latter series began in December 2007.
Figure 2 shows changes in the real prices of cigarettes from 1980.
The 1980s were characterized by high volatility. For example, between
January 1980 and June 1982, real cigarette prices increased by nearly
60% before plunging by about 80% over the next 28 months. Such extreme
price swings occurred throughout the 1980s. The abrupt drops in the real
prices of cigarettes are explained by pronounced increases in the
general inflation and not by decreases in the nominal prices of
cigarettes. Overall inflation subsided in the early 1990s and coincided
with a fairly long and sustained decrease in real cigarette prices (from
mid-1991 to the early of 1996). For the remainder of the 1990s, real
cigarette prices remained fairly stable, with the exception of one real
price hike in early 1996. Volatility in prices returned in the 2000s,
albeit to a much lesser extent than the 1980s. From 2007, trends in real
prices depend greatly on which estimates of overall inflation are used.
From 2007, Figure 2 shows estimates using the official INDEC inflation
and the estimates from the province of Sante Fe and from MIT/State
Street. Differences are obvious, substantial, and provide support to our
decision to use alternative measures of inflation.
We used data from two national surveys. First, we used data from
the Encuesta Nacional Factores de Riesgo (ENFR) conducted in 2005 and
2009. ENFR is a risk factor survey that is representative at national
and province level for urban populations (about 90% of Argentines live
in urban areas).
ENFR is a large cross-sectional survey with relatively high
response rates; in 2005 and 2009, data are available for about 45,000
(response rate, 87%) and 35,000 (response rate, 74%) individuals aged
18-65 years, respectively. Second, we used data from the Encuesta
Nacional sobre Prevalencias de Consumo de Sustancias Psicoactivas
(ENPreCoSP) conducted in 2008 and 2011. ENPreCoSP is a substance abuse
survey. As ENFR, it is representative at national and province level for
urban populations, has large sample sizes (>30,000) and relatively
high response rates (72% in 2008 and 73% in 2011). The target
population, 16-65 years, is similar to that of ENFR. Both ENFR and
ENPreCoSP used a similar multi-stratified sampling approach covering
urban areas with population over 5,000 inhabitants, and both had the
same extensive tobacco use module.
We created a measure of the age of smoking onset from the
self-reported response to the question "How old were you when you
smoked for the first time?" and include only smokers who responded
positively to the question "Have you smoked at least 100 cigarettes
in your lifetime?" ENFR data collection occurred from April to June
2005 and from October to December 2009 while ENPreCoSP data were
collected in May and June 2008 and from August to October 2011. Given
the month of interview and respondents' self-reported age of
smoking onset, it is possible to bound the age of onset within intervals
of 24 or 12 months. (3) Instead of picking the mid-point as is commonly
done, we randomly selected, using a uniform distribution, a month within
each interval. We assumed that individuals were first exposed to the
risk of starting to smoke at age 8. Consequently, individuals older than
8 in 1980 and those who reported starting smoking before age 8 were
excluded. Put differently, individuals that were older than 33, 36, 37,
and 39 when surveyed in 2005, 2008, 2009 and 2011, respectively, were
excluded. As sensitivity checks, we also assumed that individuals were
first exposed to the risk of starting to smoke at age 0,5, and 11.
Respondents with missing or nonsensical data (e.g., age of smoking
onset greater than age at time of survey) were dropped. Overall, we
dropped about 1% of individual observations from our pooled sample and
no more than 1.5% for any single survey/cycle. We also created a dataset
that kept age groups consistent across surveys and cycles by keeping
only individuals that were between 16 and 33 years old at interview.
In addition to our measure of real cigarette price that enters
models as a time varying covariate, we included measures of tobacco
control policies and a measure of real alcohol price. We included a
dummy variable to control for the introduction of Law 23.344 in May 1986
(weak ad ban and health warnings). We also created a variable that
varies across time and space to examine the effect of the introduction
of smoke-free policies at province level. To explore the possible effect
that alcohol prices may have on smoking onset, we included a measure of
average prices of alcohol beverages based on the alcohol component of
Argentina's CPI for Greater Buenos Aires. Decker and Schwartz
(2000) argue that if alcohol and cigarettes are important substitutes
(or complements), then a correctly specified cigarette demand equation
should include prices of alcoholic beverages.
To examine the potential effect of hyper- and very high inflation
on smoking onset we created dichotomous indicators. Defining hyper-,
very high, or high inflation is inherently subjective. First, we
followed Fischer, Sahay, and Vegh (2002) and used Cagan's classic
definition of hyperinflation (Cagan 1956). Cagan defines hyperinflation
"as beginning in the month the rise in prices exceeds 50 percent
and as ending in the month before the monthly rise in prices drops below
that amount and stays below for at least a year" (Fischer, Sahay,
and Vegh 2002, 840). Second, we followed Fisher et al.'s approach
to define very high inflation. Very high inflationary episode is defined
as taking place when the 12month inflation rate rises above 100%.
Cagan's definition indicates that Argentina experienced
hyperinflation from May 1989 to March 1990 while Fischer, Sahay, and
Vegh's indicates that Argentina experienced very high inflation
from July 1974 to October 1991. All other variables are time-invariant.
As a measure of socioeconomic status, we used educational attainment of
the household head (three binary indicators, primary, secondary, and
more than secondary). This measure of SES, however, is inherently
time-varying, but due to the nature of the data, enters models as a time
invariant variable. Given the relatively young age of the respondents in
our sample, we feel that SES at the time of survey is a reasonable proxy
for SES at the time at which individuals were at risk of starting
smoking. Moreover, intergenerational income mobility is relatively low
in Argentina (Jimenez and Gasparini 2010). Other individual covariates
include sex and geographical region.
B. Methods
We used discrete-time hazard models and a complementary loglog
(cloglog) specification; unlike logit or probit, cloglog has a response
curve that is asymmetric (Box-Steffensmeier and Jones 2004; Jenkins
1995; Singer and Willett 1993). One advantage of duration or survival
models is their ability to take into account observations that are
censored (in our case, individuals who have not yet started smoking at
interview). Discrete-time hazard models have been used fairly
extensively to examine the effect of prices on smoking onset (for recent
examples, see Etile and Jones 2011; Guindon 2014a; Nonnemaker and
Farrelly 2011). As a functional form for the baseline hazard function,
we used a cubic polynomial specification. As a sensitivity check, we
also used a more flexible approach and used a dummy specification for
time at risk, measured in years. An even more flexible approach would
use a dummy specification for time at risk measured in months. Such an
approach, however, requires the inclusion of a large number of unknown
parameters, is computationally demanding, and is fraught with
convergence problems.
Standard survival/duration models assume that the probability of
eventual failure is greater than zero for all individuals (Boag 1949;
Forster and Jones 2001; Schmidt and Witte 1989). Given that a large
proportion of individuals never start smoking, we also used discrete
time split population models. (4) All models were estimated using
Stata/MP 14.1 with sampling weights. Split population models were
estimated using spsurv developed by Jenkins without sampling weights.
(5)
Finally, we conducted a number of sensitivity checks to ensure that
our main results were not sensitive to alternative specifications.
First, we re-estimated models without sampling weights. In creating our
retrospective dataset, we are assuming that the collection of
individuals represented by each subject represents a collection of
individuals at the time they were at risk of starting. Second, the
assumption that the hazard function given age and other covariates does
not change with calendar time may not be valid. However, it is difficult
to identify calendar time and duration effects separately when one
includes a measure of calendar time as they are correlated by
construction. Hence, it is generally not recommended to include a
measure of calendar time in duration models. We used a somewhat less
problematic specification and estimated models that control for
differing birth cohorts (Korn, Graubard, and Midthune 1997). We used as
a calendar time predictor a variable that describes the calendar year
that corresponds to the first calendar year at which each individual was
first at risk of starting. (6) Third, we re-estimated all models using
annual data.
Generally, price elasticity estimates may be biased because of the
endogeneity of the price variable. First, when using survey data, the
problem of price endogeneity is less of a concern because no individual
tobacco user (or potential user in our case) consumes enough to
influence the market price. Second, price endogeneity is of particular
concern if one uses self-reported prices; we used a measure of tobacco
prices constructed from retail prices. Third, increases in cigarette
taxes may be proxies for unobserved sentiment against cigarette smoking
(i.e., changes in anti-smoking sentiment may drive higher taxes and
prices) (Chaloupka and Warner 2000). In the Argentinian context, it is
unlikely that changes in anti-smoking sentiment were associated with
changes in cigarette prices. As shown in Figure 2, to be correlated with
prices, anti-smoking sentiment would have had to fluctuate in an
extremely unusual fashion (e.g., decrease from the early 1990s, then
suddenly increase, followed by a sharp decrease and an even sharper
increase in the early 2000s). Moreover, there is no indication that the
national government ever had a public health objective to increase
prices (driven or not by "anti-smoking sentiment"). From the
2000s and until very recently, taxes actually decreased and the opaque
agreement between the Ministry of Finance and the tobacco industry had
for objective to reach particular tax collection objectives, not a
public health objective (see Rodriguez-Iglesias et al. 2015 for more
details). Consequently, we feel that our measure of cigarette prices is
plausibly exogenous.
III. RESULTS
Descriptive statistics are presented in Table 1. Nearly 50% of all
individuals included in our sample started smoking and, on average,
those who did were about 16 years of age. A little over half of our
sample are women, and most resided in the province (40%) or city (9%) of
Buenos Aires at interview. Approximately 40% of household heads had not
attended secondary school while less than one-fourth had studied beyond
secondary school. There are no marked differences between surveys and
cycles. Figures 3 and 4 plot, for men and women, Kaplan-Meier (KM)
product-limit survivor and hazard functions assuming that individuals
were exposed to the risk of starting to smoke at age 8. Both figures
suggest potentially important differences between men and women. Figure
4 shows clearly that the hazard rates among Argentine men and women are
non-monotonic, which provides support for our choice of functional form
for the baseline hazard function.
Tables 2-5 present the results of the discrete-time complementary
loglog (cloglog) duration models. We began with parsimonious models.
First, we only included, in addition to price, indicators for sex,
provinces, and surveys/cycles (Table 2, model 1). We then included in
turn, household's educational attainment, tobacco control policies,
alcohol prices and inflation (models 2-6). For models 7-9, as our
measure of price, we used tobacco prices that have been adjusted for
inflation using the full INDEC overall inflation series.
For covariates measured in natural logarithmic such as our price
measures, coefficients represent the elasticity of the hazard with
respect to a regressor. A negative coefficient indicates that the hazard
rate or risk of event decreases for higher values of a covariate. For
example, a price coefficient of -0.5 indicates that a 1 % increase in
the price of tobacco products would reduce the hazard or risk of smoking
onset by 0.5%. Hazard ratios (i.e., the exponential of the reported
coefficients) are more intuitive to assess the effects of dichotomous
indicators. For example, in Table 2, a hazard ratio of 0.7 (i.e.,
exp.(-0.36)) suggests that women have lower hazards to begin smoking
than men and that the difference between the hazards of men and women is
about 43%. (7)
All models suggest a fairly large, negative, and statistically
significant association between prices of tobacco products and the
hazard of smoking onset. Price elasticity estimates from our preferred
specifications (Table 2, models 1 to 6) fall within a fairly narrow
range centered around -0.45. Including additional covariates does not
alter price elasticity estimates in any significant or substantial way.
Specifications that used tobacco prices adjusted for inflation using the
full INDEC overall inflation series yield estimates that are between
about 45% and 60% higher (in absolute value) (Table 2, models 7-9).
These estimates are, however, not statistically significantly different
than estimates obtained using our alternative measure of overall
inflation.
The introduction of health warnings and advertising and promotion
regulations in May 1986 and provincial smokefree policies introduced in
the mid- to late 2000s are generally associated with a decreased hazard
of smoking initiation. Women and higher SES individuals appear to be at
lower risk of smoking onset than men and low SES individuals; the
differences between SES levels, however, are fairly small. Our measures
of hyper- and very high inflation do not, on the whole, suggest that the
hazards of smoking onset were any different in times of hyper- or very
high inflation. We found that alcohol prices have a statistically
significant and fairly large effect on the hazard of smoking onset. We
found that a 1% increase in the price of alcohol products increases the
hazard of smoking onset by about 0.7%. Provinces with relatively higher
per capita incomes, such as Patagonia (Rio Negro, Chubut, Santa Cruz,
and Tierra del Fuego) show a significantly higher hazard of smoking
onset (relative to Buenos Aires City). In contrast, relatively poor
provinces, such as Catamarca, Chaco, Formosa, Jujuy, La Rioja, Salta,
Misiones, and Santiago del Estero show a considerably lower hazard of
smoking onset. These differences occurred despite the vicinity of
Paraguay (a major source of contraband cigarettes) for provinces such as
Formosa, Misiones, and Chaco or being a major tobacco-growing province
such as Salta.
Table 3 presents results of models that examine differences in the
effect of prices on smoking onset across sex, SES, and between high and
low inflation periods. We interacted, in turn, prices with sex,
household head's highest education level, and our binary measures
of periods of hyper- or very high inflation. First, we found that women
were about twice more responsive to changes in prices than men. Second,
we did not find that low-SES individuals were more responsive than
individuals of higher SES. If anything, our findings suggest that
individuals with higher SES were more responsive to price. Lastly, we
found striking differences in the effect of prices on smoking onset
between inflation periods. We found that prices had no effects
whatsoever on smoking onset during periods of hyper- or very high
inflation.
Table 4 presents results of models that seek to examine differences
in the effect of prices by SES subgroups, between high and low inflation
periods. We found that, regardless of the SES subgroups, individuals
were not responsive to prices in very high and hyperinflation periods.
Additionally, individuals of lower SES (i.e., whose household head had
less than a secondary school education) were not found to be price
responsive in either period.
A. Sensitivity Analyses
To ensure our results were robust to alternative specifications, we
conducted a number of sensitivity checks. Table 5 presents results using
the dataset we created that retained only individuals that were between
16 and 33 years old when interviewed. Estimates of own- and cross-price
elasticities are, on the whole, robust to this alternative approach.
Results for some covariates, however, are sensitive to this
specification. First, low- and high-SES individuals do not appear to
have different hazards of smoking initiation. Second, these results do
not suggest a negative association between Law 23.344 enacted in 1986
and the hazard of smoking onset.
We also examined the robustness of our results to alternative
specifications for age when first at risk and for the functional form
for the baseline hazard function, estimated models that do not assume
that all individuals will eventually start smoking, estimated models
using annual instead of monthly data, and estimated models without
sampling weights. We re-ran selected analyses assuming individuals were
first at risk of starting to smoke at age 0, 5, and 11 years. None of
the results varied in any substantive way. This is not surprising as
only about 1% of individuals reported having started smoking before the
age of 12. As an alternative functional form for the baseline hazard
function, we used a dummy specification for time at risk, measured in
years. Here again, none of the results varied in any substantive way. We
estimated discrete time split population models and also found
relatively large. negative, and statistically significant association
between prices of tobacco products and the hazard of smoking onset. We
did found, however, on the whole, greater variations between
specifications. Using data measured annually instead of monthly did not
change our results in any significant or substantial way. On the whole,
these results were in line with results obtained using monthly data;
estimates of own-price and cross-price elasticities were similar or
somewhat higher (in absolute value). Our analyses without sampling
weights did not reveal any substantive differences in price effects.
Finally, we attempted to control for calendar year effects. With
one exception, all model specifications suggested price elasticities
that were statistically significant. On the whole, estimates of price
elasticities were somewhat lower (in absolute value); estimates of
own-price elasticities ranged from about -0.25 to -0.3 while estimates
of cross-price elasticities were fairly narrowly centered around 0.45.
All model specifications that included calendar year indicators and a
measure of very high inflation (i.e., Fisher et al.'s definition
[January 1980- 0ctober 1991]) yielded price elasticities that were not
statistically significantly different than 0.
IV. DISCUSSION
Our results suggest that tobacco prices had a statistically
significant, robust to alternative specifications (with one exception),
and fairly large impact on the hazard of smoking onset (see Figure 5 for
a graphical representation of key results). We found that prices had
little effect on the hazards of smoking onset during periods of very
high and hyperinflation, which provides some support to the notion that
prices lose their informational role in such periods. We did not find
that individuals of lower SES were more responsive to prices. If
anything, our results suggest that higher SES individuals may be more
responsive to price changes. We found some evidence that tobacco control
policies may have reduced the hazard of smoking onset.
The International Agency for Research on Cancer (2011) in its
comprehensive review concluded that most studies that jointly examined
the effect of cigarette and alcohol prices on tobacco found negative
cross-price effects, which suggested that tobacco and alcohol were
complements. Nearly all studies, however, were conducted using data from
high-income countries and none looked at the effect of alcohol prices on
smoking onset. More recently, the U.S. National Cancer Institute and the
World Health Organization (2016) concluded that evidence was too mixed
to make any definitive statement. Our results indicate that higher
alcohol prices increased the hazard of smoking onset, which suggests
that cigarettes and alcoholic beverages were substitutes. This finding
may suggest that Argentine youth that were prone to risky health
behaviors such as drinking and smoking were more likely to start smoking
when the price of other risky behaviors such as drinking increased.
This study's contributions are fourfold. First, this is just
the second exploration of the impact of prices on tobacco use that used
household- or individual-level data in any Latin America country; and
the first that examined smoking onset. Using individual data allowed us
to examine differences in the effect of prices on smoking onset across
sex and SES. Second, we used a number of alternative approaches and
found that our main result that higher tobacco prices decreased the
hazards of smoking onset was robust to most alternative specifications.
Third, we explored differences in the effect of prices during high and
low inflation periods and found that prices had no effect whatsoever on
smoking onset during periods of hyper- or very high inflation. Fourth,
we explored the effect of two sets of tobacco control policies on
smoking onset and considered the role that alcohol prices might play in
the decision to start smoking.
A. Limitations
First, using cross-sectional self-reported data to construct
smoking histories may introduce bias in the dependent variable (Taurus
and Chaloupka 1999). Although the dataset we constructed is
retrospective, the relatively young age of respondents likely reduces
the possibility of recall bias. Moreover, a number of studies have
compared reconstructed smoking prevalence rates and contemporary
measured rates and concluded that they matched relatively well,
especially when the focus was on younger populations (which is our case)
(Kenkel, Lillard, and Mathios 2004; Christopoulou et al. 2011). Heaping
(when respondents cannot recall a specific value and provide a
"prototypical" response near the actual value, resulting in
the over-representation of certain values, such as 15, 18, or 20 years
of age) can result in a mismatch between our price variable and our
dependent variable, smoking onset (Grotpeter 2008; Bar and Lillard
2012). Figure 6 presents histograms of age of onset, by survey wave.
These figures show possible heaping at 15, 18, and 20. We re-estimated
key models with three dummy variables for those who started smoking at
15, 18, and 20 and found no qualitative differences. Second, the data we
utilized allowed us to bound the age of onset within intervals of 12-24
months. This may be problematic in periods of high inflation as
month-to-month changes in prices can be substantial. Third, the nature
of the data limited our ability to include more time-varying
individual-or household-level covariates. For example, we were unable to
use a time-varying measure of household income or even a measure of
household income at time of survey. Although SES is inherently
time-varying, we used a time-invariant measure of SES (educational
attainment of the household head). Given the relatively young age of the
respondents in our sample and the relatively low levels of
inter-generational income mobility in Argentina, we feel that SES at
time of survey is a reasonable proxy for SES for the time at which
individuals were at risk of starting smoking. Nevertheless, our SES
results should be interpreted with caution. Fourth, unlike cigarette
prices that do not vary between regions in Argentina, alcohol prices do
vary between regions. Consequently, our finding that tobacco and alcohol
are substitutes should be interpreted with caution.
B. Implications for Policy and Research
This study adds to the small but growing body of evidence from LMIC
that finds that higher tobacco prices decrease the hazard of smoking
onset (Guindon 2014a; Vellios and van Walbeek 2016; Laxminarayan and
Deolalikar 2004). Of particular interest is the finding that prices have
no effect on the hazard of smoking onset in periods of hyper- and very
high inflation. During such periods, governments need to be cognizant
that their most important policy tool to reduce tobacco use (i.e., taxes
that increase real tobacco prices) is likely no longer effective. This
finding is particularly relevant to Argentina's current situation,
as its government is currently struggling to keep inflation under
control. It is also relevant to several other LMIC that are experiencing
high levels of inflation such as Belarus, Malawi, Ukraine, and Venezuela
(International Monetary Fund 2015). At the very least, governments need
to ensure that specific tobacco taxes are automatically adjusted for
changes in overall inflation as is the case in Australia and Chile.
Given that the health benefits of reduced cigarette use occur
primarily through preventing onset or cessation rather than reduced
consumption in smokers (Hart, Gruer, and Bauld 2013; Pisinger and
Godtfredsen 2007) and the lack of studies from LMIC, an extension of
this research that examines the effects of tobacco prices on cessation
could help fill an important evidence gap.
ABBREVIATIONS
CPI: Consumer Price Index
ENFR: Encuesta Nacional Factores de Riesgo
ENPreCoSP: Encuesta Nacional sobre Prevalencias de Consumo de
Sustancias Psicoactivas
FCTC: Framework Convention on Tobacco Control
IAE: Impuesto Adicional de Emergencia
IECS: Instituto de Efectividad Clinica y Sanitaria
IHME: Institute for Health Metrics and Evaluation
INDEC: Instituto Nacional de Estadistica y Censos
IPC: indice de Precios al Consumo Gran Buenos Aires
KM: Kaplan-Meier
LMIC: Low- and Middle-Income Countries
SES: Socioeconomic Status
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(1.) Price responsiveness may differ between high-income countries
and lower-income countries and between Latin American and non-Latin
American countries for a number of reasons. First, Warner (1990) argued
that price responsiveness of tobacco users in LMIC ought to be
substantially higher than users in more affluent countries because LMIC
consumers have relatively fewer resources. Second, Chaloupka et al.
(2000, 246) pointed out that "economic models of addiction suggest
that the generally lower level of education in lower-income countries is
likely to make the demand for tobacco products in these countries
relatively more responsive to changes in monetary prices than demand in
higher-income countries." Third, there is evidence that consumers
with different levels of income or wealth behave differently when it
comes to choices involving intertemporal trade-offs; poorer consumers
are likely more impatient than more affluent ones (Carvalho 2010).
Fourth, the availability and prices of substitutes and complements may
well differ between national or regional markets (e.g., local distilled
alcoholic beverages).
(2.) The Pearson correlation coefficient between CPI-tobacco and
cigarette weighted average prices for the period 1994 and 2008 is
>0.99.
(3.) The interval is only 12 months when age and age of smoking
onset are the same. (4.) A number of studies have used a split
population approach to examine the effect of prices on smoking onset.
See for example, Douglas and Hariharan (1994), Douglas (1998), Forster
and Jones (2001), Lopez Nicolas (2002), Kidd and Hopkins (2004), Madden
(2007), and Guindon (2014a).
(5.) spsurv, however, does not allow the use of sampling weights
nor does it allow the inclusion of covariates in the participation
component of the model.
(6.) We also used as a calendar time predictor the first calendar
year (measured in units of 5 years) at which each individual was first
at risk of starting (i.e., 1980-1984, 1985-1989, 1990-1994, 1995-1999,
and >2000).
(7.) As the models estimated are nonlinear, the comparison of
hazard ratios across such models with different sets of covariates
should be done with caution (Norton 2012).
G. EMMANUEL GUINDON, GUILLERMO R. PARAJE and RICARDO CHAVEZ *
This is an open access article under the terms of the Creative
Commons Attribution-NonCommercial-NoDerivs License, which permits use
and distribution in any medium, provided the original work is properly
cited, the use is non-commercial and no modifications or adaptations are
made.
* We thank Daniel Araya, Jorge Vives, and Mathieu Poirier for their
research assistance and K. Stephen Brown, Frank J. Chaloupka, David
Feeny, Christina Hackett, Emily McGirr, and members of McMaster
University's Polinomics Group for their comments and discussion.
Funding: International Development Research Centre (grants 106836-001
and 107206-001) and the Canadian Cancer Society (grant 702176 to GEG).
GEG holds the Centre for Health Economics and Policy Analysis
(CHEPA)/Ontario Ministry of Health and Long-Term Care (MOHLTC) Chair in
Health Equity, an endowed Chair funded in part by the MOHLTC. The
funders had no role in the study design, analysis, interpretation,
writing of the report, or in the decision to submit this article for
publication.
Guindon: Assistant Professor, Centre for Health Economics and
Policy Analysis; Department of Health Research Methods, Evidence, and
Impact; Department of Economics, McMaster University, Hamilton L8S 4K1,
Canada. Phone +1 905 525 9140x22879, Fax +1 905 522 9507, E-mail
emmanuel.guindon@mcmaster.ca
Paraje: Professor, Escuela de Negocios, Universidad Adolfo Ibanez,
Santiago de Chile, Chile. Phone +56 2 2331 1380, Fax +56 2 2278 4413,
E-mail guillermo.paraje@uai.cl
Chavez: Economist, Banco Central del Ecuador, Quito 170409,
Ecuador. Phone +593 2393 8600, Fax +593 2393 8600, E-mail
rechavez@bce.ec
doi: 10.1111/ecin.12490
Online Early publication August 16, 2017
Caption: FIGURE 1 Timeline of Key Tobacco Control Measures and of
Political/Monetary/Inflation Events
Caption: FIGURE 2 Inflation-Adjusted Manufactured Tobacco Prices,
1980-2014
Caption: FIGURE 3 Kaplan-Meier Survivor Functions for Starting
Smoking--Men and Women (assuming individuals were first exposed to the
risk of starting at age 8)
Caption: FIGURE 4 Hazard Functions for Starting Smoking--Men and
Women (assuming individuals were first exposed to the risk of starting
at age 8)
Caption: FIGURE 5 Results, Own-Price Elasticities for Cigarettes;
Differences in the Effect of Prices on Smoking Onset Across Sex,
Socioeconomic Status and Between High and Low Inflation Periods
Caption: FIGURE 6 The Distribution of Self-Reported Age of Smoking
Initiation, by Survey Cycle
TABLE 1
Descriptive Statistics
Variable ENFR, 2005 ENPreCoSP, 2008
Mean (SD) Mean (SD)
Age of starting smoking 16.0 15.9
(ever smokers only) (2.6) % -2.80%
Smoking onset 45.1 38.0
Sex, male 49.4 47.3
Education, household head
Primary 43.3 40.8
Secondary 35.7 38.2
>Secondary 21.0 21.0
Province
Buenos Aires, city 9.6 8.8
Buenos Aires, prov 39.8 39.7
Catam area 0.8 0.9
Cordoba 8.7 8.1
Corrientes 2.5 2.5
Chaco 2.3 2.4
Chubut 1.1 1.1
Entre Rios 2.9 2.9
Formosa 1.0 1.0
Jujuy 1.7 1.8
La Pampa 0.6 0.7
La Rioja 0.9 0.9
Mendoza 3.7 3.9
Misiones 1.9 2.2
Neuquen 1.3 1.3
Rio Negro 1.3 1.3
Salta 3.0 3.0
San Juan 1.8 1.7
San Luis 1.0 1.1
Santa Cruz 0.5 0.6
Santa Fe 8.0 8.6
Santiago del Estero 1.5 1.6
Tucuman 3.8 3.5
Tierra del Fuego 0.3 0.4
Number of individuals 13,607 16,120
Number of observations 2,206,845 2,775,512
Variable ENFR, 2009 ENPreCoSP, 2011
Mean (SD) Mean (SD)
Age of starting smoking 16.3 16.2
(ever smokers only) -3.00% -3.00%
Smoking onset 41.9 38.6
Sex, male 48.9 50.5
Education, household head
Primary 38.3 36.6
Secondary 37.6 38.0
>Secondary 24.2 25.4
Province
Buenos Aires, city 9.2 7.9
Buenos Aires, prov 40.4 40.5
Catam area 0.9 0.9
Cordoba 8.1 8.4
Corrientes 2.5 2.4
Chaco 2.4 2.5
Chubut 1.1 1.2
Entre Rios 2.9 3.0
Formosa 1.1 1.2
Jujuy 1.7 1.7
La Pampa 0.7 0.7
La Rioja 0.9 0.9
Mendoza 3.9 4.0
Misiones 2.1 2.2
Neuquen 1.4 1.4
Rio Negro 1.3 1.4
Salta 3.0 3.0
San Juan 1.7 1.7
San Luis 1.1 1.1
Santa Cruz 0.6 0.6
Santa Fe 7.6 7.8
Santiago del Estero 1.6 1.7
Tucuman 3.6 3.6
Tierra del Fuego 0.4 0.4
Number of individuals 14,382 18,560
Number of observations 2,624,445 3,412,101
Notes: Standard deviations in parenthesis.
ENFR, Encuesta Nacional Factores de Riesgo;
ENPreCoSP, Encuesta Nacional sobre Prevalencias
de Consumo de Sustancias Psicoactivas.
TABLE 2
Discrete-Time Complementary loglog (cloglog)
Duration Models of Smoking Initiation, Full Sample
Model
Variables (1) (2)
Prices (in In)
Tobacco, sf -0.466 *** -0.468 ***
(0.092) (0.092)
Tobacco, indec
Alcohol, sf
Alcohol, indec
Inflation (ref, no inflation)
Hyperinflation
Very high inflation
Sex (ref, male)
-0.357 *** -0.357 ***
Education, hh head (0.025) (0.025)
(ref. primary)
Secondary 0.021
>Secondary (0.030)
-0.059 *
Tobacco control policies (0.033)
Law 23.344, May 1986
Smokefree policies
Province
(ref, Buenos Aires city)
Buenos Aires, prov -0.023 -0.045
Catamarca (0.047) (0.047)
-0.295 *** -0.318 ***
Cordoba (0.055) (0.056)
-0.135 ** -0.151 ***
Corrientes (0.055) (0.055)
-0.355 *** -0.374 ***
Chaco (0.056) (0.057)
-0.264 *** -0.286 ***
Chubut (0.054) (0.056)
0.132 ** 0.109 **
Entre Rios (0.054) (0.055)
-0.153 *** -0.176 ***
Formosa (0.056) (0.057)
-0.631 *** -0.654 ***
Jujuy (0.062) (0.063)
-0.407 *** -0.431 ***
La Pampa (0.057) (0.058)
0.096 * 0.077
La Rioja (0.055) (0.056)
-0.164 *** -0.185 ***
Mendoza (0.052) (0.053)
0.057 0.040
Misiones (0.054) (0.054)
-0.353 *** -0.376 ***
Neuquen (0.057) (0.058)
0.089 0.069
Rio Negro (0.055) (0.055)
0.128 ** 0.105 *
Salta (0.053) (0.054)
-0.189 *** -0.212 ***
San Juan (0.055) (0.056)
-0.075 -0.098 *
San Luis (0.054) (0.055)
0.080 0.059
Santa Cruz (0.052) (0.053)
0.290 *** 0.266 ***
Santa Fe (0.051) (0.052)
-0.007 -0.025
Santiago del Estero (0.054) (0.055)
-0.329 *** -0.353 ***
Tucuman (0.057) (0.058)
-0.087 -0.106 **
(0.053) (0.054)
Tierra del Fuego 0.221 *** 0.198 ***
(0.053) (0.053)
Survey/cycle (ref,
ENPreCoSP. 2011)
ENFR 2005 0.172 *** 0.169 ***
(0.037) (0.037)
ENPreCoSP, 2008 0.020 0.017
(0.037) (0.037)
ENFR, 2009 0.064 ** 0.063 **
(0.031) (0.031)
Duration dependency
t 0.155 *** 0.155 ***
(0.003) (0.003)
t^2 -0.001*** -0.001 ***
(0.000) (0.000)
t^3 0.000 *** 0.000 ***
(0.000) (0.000)
Intercept -11.596 *** -11.569 ***
(0.139) (0.143)
Model
Variables (3) (4)
Prices (in In)
Tobacco, sf -0.432 *** -0.425 ***
(0.093) (0.093)
Tobacco, indec
Alcohol, sf
Alcohol, indec 0.705 ***
(0.149)
Inflation (ref, no inflation)
Hyperinflation
Very high inflation
Sex (ref, male)
-0.358 *** -0.361 ***
Education, hh head (0.025) (0.025)
(ref. primary)
Secondary 0.020 0.018
>Secondary (0.030) (0.030)
-0.061 * -0.065 **
Tobacco control policies (0.033) (0.033)
Law 23.344, May 1986 -0.176 -0.235 *
Smokefree policies (0.121) (0.121)
-0.196 *** -0.162 ***
Province (0.062) (0.062)
(ref, Buenos Aires city)
Buenos Aires, prov -0.046 -0.046
Catamarca (0.047) (0.047)
-0.319 *** -0.318 ***
Cordoba (0.056) (0.056)
-0.109 ** -0.117 **
Corrientes (0.056) (0.056)
-0.376 *** -0 374 ***
Chaco (0.057) (0.057)
-0.287 *** -0.285 ***
Chubut (0.056) (0.056)
0.108 ** 0.107 *
Entre Rios (0.055) (0.055)
-0.170 *** -0 172 ***
Formosa (0.057) (0.057)
-0.656 *** -0.655 ***
Jujuy (0.063) (0.063)
-0.432 *** -0.432 ***
La Pampa (0.058) (0.058)
0.076 0.076
La Rioja (0.056) (0.056)
-0.186 *** -0.185 ***
Mendoza (0.053) (0.053)
0.039 0.040
Misiones (0.054) (0.054)
-0.378 *** -0.376 ***
Neuquen (0.058) (0.058)
0.079 0.079
Rio Negro (0.055) (0.055)
0.104 * 0.105 *
Salta (0.054) (0.054)
-0.214 *** -0.213 ***
San Juan (0.056) (0.056)
-0.072 -0.077
San Luis (0.055) (0.055)
0.057 0.058
Santa Cruz (0.053) (0.053)
0.265 *** 0.266 ***
Santa Fe (0.052) (0.052)
-0.001 -0.003
Santiago del Estero (0.056) (0.056)
-0.349 *** -0.349 ***
Tucuman (0.058) (0.058)
-0.081 -0.086
(0.055) (0.055)
Tierra del Fuego 0.197 *** 0.197 ***
(0.053) (0.053)
Survey/cycle (ref,
ENPreCoSP. 2011)
ENFR 2005 0.161 *** 0.148 ***
(0.037) (0.037)
ENPreCoSP, 2008 0.013 0.005
(0.037) (0.037)
ENFR, 2009 0.060 * 0.053 *
(0.031) (0.031)
Duration dependency
t 0.154 *** 0.154 ***
(0.003) (0.003)
t^2 -0.001 *** -0.001 ***
(0.000) (0.000)
t^3 0.000 *** 0.000 ***
(0.000) (0.000)
Intercept -11.541 *** -14.823 ***
(0.148) (0.703)
Model
Variables (5) (6)
Prices (in In)
Tobacco, sf -0.467 *** -0.409 ***
(0.105) (0.094)
Tobacco, indec
Alcohol, sf
Alcohol, indec 0.715 *** 0.715 ***
(0.148) (0.150)
Inflation (ref, no inflation)
Hyperinflation -0.098
(0.073)
Very high inflation 0.039
Sex (ref, male) (0.042)
-0.362 *** -0.360 ***
Education, hh head (0.025) (0.025)
(ref. primary)
Secondary 0.017 0.019
>Secondary (0.030) (0.030)
-0.066 ** -0.064 **
Tobacco control policies (0.033) (0.033)
Law 23.344, May 1986 -0.265 ** -0.240 **
Smokefree policies (0.125) (0.121)
-0.152 ** -0.166 ***
Province (0.063) (0.062)
(ref, Buenos Aires city)
Buenos Aires, prov -0.045 -0.046
Catamarca (0.047) (0.047)
-0.318 *** -0.318 ***
Cordoba (0.056) (0.056)
-0.118 ** -0.116 **
Corrientes (0.056) (0.056)
-0.373 *** -0.375 ***
Chaco (0.057) (0.057)
-0.285 *** -0.286 ***
Chubut (0.056) (0.056)
0.107 * 0.108 **
Entre Rios (0.055) (0.055)
-0.172 *** -0 172 ***
Formosa (0.057) (0.057)
-0.654 *** -0.655 ***
Jujuy (0.063) (0.063)
-0.431 *** -0.432 ***
La Pampa (0.058) (0.058)
0.076 0.076
La Rioja (0.056) (0.056)
-0.184 *** -0.186 ***
Mendoza (0.053) (0.053)
0.041 0.040
Misiones (0.054) (0.054)
-0.375 *** -0.376 ***
Neuquen (0.058) (0.058)
0.078 0.079
Rio Negro (0.055) (0.055)
0.105 * 0.105 *
Salta (0.054) (0.054)
-0.212 *** -0.213 ***
San Juan (0.056) (0.056)
-0.078 -0.077
San Luis (0.055) (0.055)
0.058 0.058
Santa Cruz (0.053) (0.053)
0.266 *** 0.266 ***
Santa Fe (0.052) (0.052)
-0.004 -0.003
Santiago del Estero (0.056) (0.056)
-0.348 *** -0.349 ***
Tucuman (0.058) (0.058)
-0.087 -0.085
(0.055) (0.055)
Tierra del Fuego 0.197 *** 0.197 ***
(0.053) (0.053)
Survey/cycle (ref,
ENPreCoSP. 2011)
ENFR 2005 0.144 *** 0.150 ***
(0.037) (0.037)
ENPreCoSP, 2008 0.003 0.005
(0.037) (0.037)
ENFR, 2009 0.051 * 0.053 *
(0.031) (0.031)
Duration dependency
t 0.154 *** 0.154 ***
(0.003) (0.003)
t^2 -0.001 *** -0.001 ***
(0.000) (0.000)
t^3 0.000 *** 0.000 ***
(0.000) (0.000)
Intercept -14.863 *** -14.870 ***
(0.700) (0.707)
Model
Variables (7) (8)
Prices (in In)
Tobacco, sf
Tobacco, indec -0.674 *** -0.637 ***
Alcohol, sf (0.081) (0.085)
Alcohol, indec -0.039
(0.149)
Inflation (ref, no inflation)
Hyperinflation -0.058
(0.073)
Very high inflation
Sex (ref, male)
-0.358 *** -0.358 ***
Education, hh head (0.025) (0.025)
(ref. primary)
Secondary 0.020
>Secondary (0.030)
-0.063 *
Tobacco control policies (0.033)
Law 23.344, May 1986 -0.175
Smokefree policies (0.121)
-0.127 **
Province (0.063)
(ref, Buenos Aires city)
Buenos Aires, prov -0.021 -0.046
Catamarca (0.047) (0.047)
-0.291 *** -0.317 ***
Cordoba (0.055) (0.056)
-0.135 ** -0.125 **
Corrientes (0.055) (0.056)
-0.352 *** -0.375 ***
Chaco (0.056) (0.057)
-0.260 *** -0.285 ***
Chubut (0.054) (0.056)
0.135 ** 0.110 **
Entre Rios (0.054) (0.055)
-0.151 *** -0.172 ***
Formosa (0.056) (0.057)
-0.625 *** -0.652 ***
Jujuy (0.062) (0.063)
-0.404 *** -0.431 ***
La Pampa (0.057) (0.058)
0.100 * 0.079
La Rioja (0.055) (0.056)
-0.160 *** -0.184 ***
Mendoza (0.052) (0.053)
0.058 0.040
Misiones (0.054) (0.054)
-0.347 *** --0.374 ***
Neuquen (0.057) (0.058)
0.092 * 0.077
Rio Negro (0.055) (0.055)
0.131 ** 0.107 **
Salta (0.053) (0.054)
-0.186 *** -0.212 ***
San Juan (0.055) (0.056)
-0.073 -0.082
San Luis (0.054) (0.055)
0.083 0.059
Santa Cruz (0.052) (0.053)
0.294 *** 0.268 ***
Santa Fe (0.051) (0.052)
-0.006 -0.009
Santiago del Estero (0.054) (0.056)
-0.325 *** -0.349 ***
Tucuman (0.057) (0.058)
-0.086 -0.090 *
(0.0531 (0.0551
Tierra del Fuego 0.223 *** 0.198 ***
(0.053) (0.053)
Survey/cycle (ref,
ENPreCoSP. 2011)
ENFR 2005 0.143 *** 0.138 ***
(0.037) (0.037)
ENPreCoSP, 2008 -0.002 -0.006
(0.037) (0.037)
ENFR, 2009 0.045 0.044
(0.031) (0.031)
Duration dependency
t 0.155 *** 0.155 ***
(0.003) (0.003)
t^2 -0.001 *** -0.001 ***
(0.000) (0.000)
t^3 0.000 *** 0.000 ***
(0.000) (0.000)
Intercept -11.533 *** -11.291 ***
(0.138) (0.694)
Model
Variables (9)
Prices (in In)
Tobacco, sf
Tobacco, indec -0.708 ***
Alcohol, sf (0.093)
Alcohol, indec -0.000
(0.150)
Inflation (ref, no inflation)
Hyperinflation
Very high inflation 0.067
Sex (ref, male) (0.041)
-0.360 ***
Education, hh head (0.025)
(ref. primary)
Secondary 0.017
>Secondary (0.030)
-0.066 **
Tobacco control policies (0.033)
Law 23.344, May 1986 -0.226 *
Smokefree policies (0.125)
-0.106 *
Province (0.064)
(ref, Buenos Aires city)
Buenos Aires, prov -0.044
Catamarca (0.047)
-0.316 ***
Cordoba (0.056)
-0.129 **
Corrientes (0.056)
-0.373 ***
Chaco (0.057)
-0.283 ***
Chubut (0.056)
0.109 **
Entre Rios (0.055)
--0.172 ***
Formosa (0.057)
-0.651 ***
Jujuy (0.063)
-0.429 ***
La Pampa (0.058)
0.078
La Rioja (0.056)
-0.182 ***
Mendoza (0.053)
0.041
Misiones (0.054)
-0.372 ***
Neuquen (0.058)
0.076
Rio Negro (0.055)
0.107 **
Salta (0.054)
-0.210 ***
San Juan (0.056)
-0.084
San Luis (0.055)
0.060
Santa Cruz (0.053)
0.268 ***
Santa Fe (0.052)
-0.010
Santiago del Estero (0.056)
-0 347 ***
Tucuman (0.058)
-0.092 *
(0.055)
Tierra del Fuego 0.198 ***
(0.053)
Survey/cycle (ref,
ENPreCoSP. 2011)
ENFR 2005 0.129 ***
(0.037)
ENPreCoSP, 2008 -0.010
(0.037)
ENFR, 2009 0.040
(0.031)
Duration dependency 0.155 ***
t
(0.003)
t^2 -0.001 ***
(0.000)
t^3 0.000 ***
(0.000)
Intercept -11.467 ***
(0.696)
Notes: Robust standard errors in parentheses,
indec, prices adjusted for inflation using official
inflation data from INDEC; sf, prices adjusted for
inflation using inflation data from the Santa Fe
statistical office; hyperinflation defined using Cagan's
definition (May 1989 to March 1990); very high inflation
defined using Fisher et al.'s definition (January 1980 to
October 1991); hh. household head; ENFR, Encuesta Nacional
Factores de Riesgo; ENPreCoSP, Encuesta Nacional sobre
Prevalencias de Consumo de Sustancias Psicoactivas.
Number of observations, 10,996,654; number
of failures, 25, 696.
*, **, and ***, significant at 10%, 5%,
and 1%, respectively.
TABLE 3
Discrete-Time Complementary loglog (cloglog)
Duration Models of Smoking Initiation, Full Sample
Model Variables (1) (2)
Prices (in In)
Tobacco, sf -0.304 ** -0.212
(0.133) (0.171)
Alcohol, sf 0.710 *** 0.708 ***
(0.148) (0.148)
Inflation (ref, no inflation)
Very high inflation 0.040 0.042
(0.042) (0.042)
Hyperinflation
Sex (ref, male)
-0.223 *** -0.362 ***
(0.072) (0.025)
Education, hh head (ref, primary)
Secondary 0.017 0.145 *
>Secondary (0.030) (0.085)
-0.066 ** 0.1 44
(0.033) (0.090)
Tobacco control policies
Law 23.344, May 1986 -0.266 ** -0.268 **
Smokefree policies (0.125) (0.125)
-0.153 ** -0.152 **
(0.063) (0.063)
Province (ref, Buenos Aires city)
Buenos Aires, prov -0.045 -0.045
Catam area (0.047) (0.047)
-0.318 *** -0.317 ***
Cordoba (0.056) (0.056)
-0.117 ** -0.119 **
Corrientes (0.056) (0.056)
-0.373 *** -0.373 ***
Chaco (0.057) (0.057)
-0.284 *** -0.285 ***
Chubut (0.056) (0.056)
0.107 * 0.107 *
Entre Rios (0.055) (0.055)
-0.171 *** -0.172 ***
Formosa (0.057) (0.057)
-0.655 *** -0.654 ***
Jujuy (0.063) (0.063)
-0.432 *** -0.431 ***
La Pampa (0.058) (0.058)
0.075 0.075
La Rioja (0.056) (0.056)
-0.185 *** -0.184 ***
Mendoza (0.053) (0.053)
0.041 0.041
Misiones (0.054) (0.054)
-0.375 *** -0.376 ***
Neuquen (0.058) (0.058)
0.078 0.078
Rio Negro (0.055) (0.055)
0.105 * 0.105 *
Salta (0.054) (0.054)
-0.212 *** -0.212 ***
San Juan (0.056) (0.056)
-0.078 -0.078
San Luis (0.055) (0.055)
0.058 0.058
Santa Cruz (0.053) (0.053)
0.266 *** 0.267 ***
Santa Fe (0.052) (0.052)
-0.004 -0.004
(0.056) (0.056)
Santiago del Estero -0.348 *** -0.347 ***
(0.058) (0.058)
Tucuman -0.087 -0.086
(0.055) (0.055)
Tierra del Fuego 0.196 *** 0.197 ***
(0.053) (0.053)
Survey/cycle
(ref, ENPreCoSP, 2011)
ENFR 2005 0.145 *** 0.147 ***
(0.037) (0.037)
ENPreCoSP, 2008 0.004 0.004
(0.037) (0.037)
ENFR, 2009 0.052 * 0.052 *
(0.031) (0.031)
Duration dependency
t 0.154 *** 0.154 ***
(0.003) (0.003)
t^2 -0.001 *** -0.001 ***
(0.000) (0.000)
t^3 0.000 *** 0.000 ***
(0.000) (0.000)
Interactions
Tob. price x sex -0.368 **
(0.183)
Tob. price X secondary -0.336
(0.214)
Tob. price x > secondary -0.559 **
(0.227)
Tob. price x very high inflation
Tob. price x very hyper inflation
Intercept -14.903 *** -14.930 ***
(0.699) (0.702)
Own-price elasticities for tobacco
Sex: male -0.304 **
(0.132)
Sex: female -0.672 ***
(0.146)
hh education: primary -0.212
(0.171)
hh education: secondary -0.548 ***
(0.148)
hh education: > secondary -0.770 ***
(0.168)
Hyper, very high inflation: yes
Hyper, very high inflation: no
Model Variables (3) (4)
Prices (in In)
Tobacco, sf -0.675 *** -0.451 ***
(0.130) (0.102)
Alcohol, sf 0.747 *** 0.721 ***
(0.147) (0.150)
Inflation (ref, no inflation)
Very high inflation -0.330 ***
(0.116)
Hyperinflation -0.310 **
Sex (ref, male) (0.147)
-0.363 *** -0.360 ***
(0.025) (0.025)
Education, hh head (ref, primary)
Secondary 0.016 0.019
>Secondary (0.030) (0.030)
-0.068 ** -0.064 **
(0.033) (0.033)
Tobacco control policies
Law 23.344, May 1986 -0.171 -0.24 1**
Smokefree policies (0.129) (0.121)
-0.121 * -0.160 **
(0.064) (0.063)
Province (ref, Buenos Aires city)
Buenos Aires, prov -0.044 -0.046
Catam area (0.047) (0.047)
-0.317 *** -0.318 ***
Cordoba (0.056) (0.056)
-0.125 ** -0.117 **
Corrientes (0.056) (0.056)
-0.372 *** -0.375 ***
Chaco (0.057) (0.057)
-0.283 *** -0.286 ***
Chubut (0.056) (0.056)
0.107 * 0.108 **
Entre Rios (0.055) (0.055)
-0.173 *** -0.172 ***
Formosa (0.057) (0.057)
-0.652 *** -0.654 ***
Jujuy (0.063) (0.063)
-0.431 *** -0.432 ***
La Pampa (0.058) (0.058)
0.077 0.076
La Rioja (0.056) (0.056)
-0.183 *** -0.186 ***
Mendoza (0.053) (0.053)
0.042 0.040
Misiones (0.054) (0.054)
-0.373 *** -0.376 ***
Neuquen (0.058) (0.058)
0.077 0.078
Rio Negro (0.055) (0.055)
0.105 * 0.105 *
Salta (0.054) (0.054)
-0.211 *** -0.213 ***
San Juan (0.056) (0.056)
-0.082 -0.077
San Luis (0.055) (0.055)
0.059 0.058
Santa Cruz (0.053) (0.053)
0.267 *** 0.266 ***
Santa Fe (0.052) (0.052)
-0.007 -0.004
(0.056) (0.056)
Santiago del Estero -0.348 *** -0.349 ***
(0.058) (0.058)
Tucuman -0.091 * -0.086
(0.055) (0.055)
Tierra del Fuego 0.197 *** 0.197 ***
(0.053) (0.053)
Survey/cycle
(ref, ENPreCoSP, 2011)
ENFR 2005 0.135 *** 0.149 ***
(0.037) (0.037)
ENPreCoSP, 2008 0.001 0.005
(0.037) (0.037)
ENFR, 2009 0.049 0.053 *
(0.031) (0.031)
Duration dependency 0.154 ***
t 0.154 ***
(0.003) (0.003)
t^2 -0.001 *** -0.001 ***
(0.000) (0.000)
t^3 0.000 *** 0.000 ***
(0.000) (0.000)
Interactions
Tob. price x sex
Tob. price X secondary
Tob. price x > secondary
Tob. price x very high inflation 0.768 ***
(0.224)
Tob. price x very hyper inflation 0.430 *
(0.256)
Intercept -14.949 *** -14.880 ***
(0.695) (0.708)
Own-price elasticities for tobacco
Sex: male
Sex: female
hh education: primary
hh education: secondary
hh education: > secondary
Hyper, very high inflation: yes 0.092 -0.020
(0.183) (0.235)
Hyper, very high inflation: no -0.675 *** -0.450 ***
(0.130) (0.102)
Notes: Robust standard errors in parentheses;
hyperinflation defined using Cagan's definition
(May 1989 to March 1990); very high inflation defined
using Fisher et al.'s definition (January 1980 to
October 1991); hh, household head; ENFR, Encuesta
Nacional Factores de Riesgo; ENPreCoSP, Encuesta
Nacional sobre Prevalencias de Consumo de Sustancias
Psicoactivas. Number of observations, 10,996,654;
number of failures, 25,696.
*, **, and ***, significant at 10%, 5% and 1%, respectively.
TABLE 4
Discrete-Time Complementary loglog (cloglog) Duration Models
of Smoking Initiation, Full Sample--Differences Across High
and Low Inflation Periods, by Socioeconomic Status (SES)
Model (1) (2) (3)
SES category Low Mid High
Very high inflation
Variables
Prices (in In)
Tobacco, sf -0.013 0.264 -0.060
Alcohol, sf (0.346) (0.266) (0.326)
0.561 ** 0.817 *** 0.856 ***
Inflation (0.251) (0.240) (0.270)
(ref, no inflation)
Very high inflation -0.079 -0.501*** -0.437**
(0.221) (0.169) (0.199)
Hyperinflation
Sex (ref, male)
-0.494 *** -0.356 *** -0.154 ***
Tobacco control policies (0.045) (0.040) (0.046)
Law 23.344, May 1986 -0.126 -0.141 -0.300
(0.191) (0.214) (0.310)
Smokefree policies -0.183 * -0.052 -0.133
Province (0.105) (0.105) (0.126)
(ref, Buenos Aires city)
Buenos Aires, prov 0.227 -0.072 -0.063
Catamarca (0.139) (0.079) (0.073)
-0.056 -0.377 *** -0.252 ***
Cordoba (0.148) (0.092) (0.094)
0.268 * -0.205 ** -0.238 ***
Corrientes (0.150) (0.095) (0.087)
-0.057 -0.447 *** -0.445 ***
Chaco (0.146) (0.097) (0.093)
0.022 -0.284 *** -0.485 ***
Chubut (0.144) (0.092) (0.098)
0.410 *** 0.107 -0.083
Entre Rios (0.145) (0.088) (0.106)
0.183 -0.300 *** -0.226 **
Formosa (0.147) (0.093) (0.099)
-0.389 ** -0.722 *** -0.565 ***
Jujuy (0.151) (0.100) (0.128)
-0.302 ** -0.330 *** -0.393 ***
La Pampa (0.151) (0.092) (0.100)
0.364 ** 0.081 -0.075
La Rioja (0.148) (0.092) (0.093)
0.146 -0.286 *** -0.191 **
Mendoza (0.144) (0.088) (0.082)
0.411 *** 0.011 -0.139
Misiones (0.147) (0.090) (0.087)
-0.064 -0.437 *** -0.462 ***
Neuquen (0.146) (0.096) (0.103)
0.302 ** 0.107 0.002
Rio Negro (0.150) (0.090) (0.086)
0.359 ** 0.129 -0.036
Salta (0.144) (0.088) (0.097)
0.020 -0.228 ** -0.163 *
San Juan (0.146) (0.090) (0.091)
0.215 -0.083 -0.246**
San Luis (0.145) (0.090) (0.102)
0.317 ** 0.034 0.020
Santa Cruz (0.144) (0.086) (0.085)
0.484 *** 0.324 *** 0.095
Santa Fe (0.145) (0.082) (0.088)
0.276 * 0.004 -0.131
Santiago del Estero (0.148) (0.093) (0.092)
-0.125 -0.326 *** -0.331 ***
(0.147) (0.094) (0.109)
Tucuman 0.285 ** -0.226 ** -0.207 **
(0.143) (0.095) (0.089)
Tierra del Fuego 0.490 *** 0.170 ** 0.125
(0.154) (0.084) (0.086)
Survey/cycle
(ref, ENPreCoSP, 2011)
ENFR 2005 0.148 ** 0.098 * 0.215 ***
(0.065) (0.058) (0.068)
ENPreCoSP, 2008 0.019 -0.060 0.107 *
(0.066) (0.058) (0.065)
ENFR, 2009 0.098 * 0.036 0.008
(0.053) (0.048) (0.059)
Duration dependency
t 0.136 *** 0.163 *** 0]79 ***
(0.005) (0.005) (0.006)
t^2 -0.001 *** -0.001 *** -0.001 ***
(0.000) (0.000) (0.000)
t^3 0.000 *** 0.000 *** 0.000 ***
(0.000) (0.000) (0.000)
Interactions
Tob. price x very high -0.277 -1.237 *** -0.754 *
inflation (0.411) (0.333) (0.402)
Tob. price X very
hyper inflation
Intercept -13.6 *** -15.5 *** -16.7 ***
(1.179) (1.130) (1.286)
Own-price elasticities
for tobacco
Hyper, very high -0.013 0.264 -0.060
inflation: yes (0.346) (0.266) (0.326)
Hyper, very high -0.290 -0.971 *** -0.813 ***
inflation: no (0.228) (0.201) (0.231)
Number of observations 3,754,776 4,312,961 2,928,917
Number of failures 8,731 10,567 6,398
Model (4) (5) (6)
SES category Low Mid High
Hyperinflation
Variables
Prices (in In)
Tobacco, sf -0.134 -0.187 0.345
Alcohol, sf (0.398) (0.389) (0.434)
0.539 ** 0.774 *** 0.858 ***
Inflation (0.256) (0.246) (0.270)
(ref, no inflation)
Very high inflation
Hyperinflation -0.104 -0.355 -0.540 **
Sex (ref, male) (0.248) (0.246) (0.266)
-0.492 *** -0.350 *** -0.155 ***
Tobacco control policies (0.045) (0.040) (0.046)
Law 23.344, May 1986 -0.122 -0.233 -0.451
(0.174) (0.200) (0.306)
Smokefree policies -0.209 ** -0.135 -0.122
Province (0.102) (0.102) (0.122)
(ref, Buenos Aires city)
Buenos Aires, prov 0.226 -0.075 -0.062
Catamarca (0.139) (0.079) (0.073)
-0.057 -0.380 *** -0.252 ***
Cordoba (0.148) (0.092) (0.094)
0.274 * -0.188 ** -0.239 ***
Corrientes (0.149) (0.095) (0.086)
-0.060 -0.448 *** -0.443 ***
Chaco (0.146) (0.097) (0.093)
0.020 -0.287 *** -0.485 ***
Chubut (0.144) (0.092) (0.098)
0.410 *** 0.110 -0.085
Entre Rios (0.145) (0.088) (0.106)
0.183 -0.297 *** -0.226 **
Formosa (0.147) (0.093) (0.099)
-0.391 *** -0.723 *** -0.566 ***
Jujuy (0.151) (0.100) (0.128)
-0.303 ** -0.332 *** -0.393 ***
La Pampa (0.151) (0.092) (0.100)
0.364 ** 0.082 -0.075
La Rioja (0.148) (0.092) (0.093)
0.145 -0.288 *** -0.189 **
Mendoza (0.144) (0.088) (0.082)
0.410 *** 0.009 -0.139
Misiones (0.147) (0.090) (0.087)
-0.066 -0.441 *** -0.460 ***
Neuquen (0.146) (0.096) (0.103)
0.302 ** 0.112 0.001
Rio Negro (0.150) (0.090) (0.086)
0.356 ** 0.132 -0.038
Salta (0.144) (0.088) (0.097)
0.018 -0.229 ** -0.162 *
San Juan (0.146) (0.090) (0.091)
0.218 -0.073 -0.248**
San Luis (0.145) (0.090) (0.102)
0.316 ** 0.035 0.021
Santa Cruz (0.144) (0.086) (0.085)
0.482 *** 0.322 *** 0.093
Santa Fe (0.145) (0.082) (0.088)
0.277* 0.014 -0.130
Santiago del Estero (0.148) (0.093) (0.092)
-0.125 -0.328 *** -0.330 ***
(0.147) (0.094) (0.109)
Tucuman 0.289 ** -0.214 ** -0.207 **
(0.143) (0.095) (0.089)
Tierra del Fuego 0.488 *** 0.170 ** 0.123
(0.154) (0.084) (0.086)
Survey/cycle
(ref, ENPreCoSP, 2011)
ENFR 2005 0.159 ** 0.122 ** 0.211 ***
(0.064) (0.058) (0.067)
ENPreCoSP, 2008 0.023 -0.055 0.105
(0.066) (0.058) (0.065)
ENFR, 2009 0.101 * 0.043 0.006
(0.053) (0.048) (0.059)
Duration dependency 0.179 ***
t 0.136 *** 0.163 ***
(0.005) (0.005) (0.006)
t^2 -0.001 *** -0.001 *** -0.001 ***
(0.000) (0.000) (0.000)
t^3 0.000 *** 0.000 *** 0.000 ***
(0.000) (0.000) (0.000)
Interactions
Tob. price x very high
inflation
Tob. price X very -0.013 -0.321 -1.195 **
hyper inflation (0.436) (0.420) (0.468)
Intercept -13.6 *** -15.4 *** -16.7 ***
(1.205) (1.158) (1.284)
Own-price elasticities -0.186 0.344
for tobacco
Hyper, very high -0.133
inflation: yes (0.397) (0.388) (0.433)
Hyper, very high -0.146 -0.506 *** -0.849 ***
inflation: no (0.179) (0.160) (0.176)
Number of observations 3,754,776 4,312,961 2,928,917
Number of failures 8,731 10,567 6,398
Notes: Robust standard errors in parentheses;
hyperinflation defined using Cagan's definition
(May 1989 to March 90); very high inflation defined
using Fisher et al.'s definition (January 1980 to October
1991); hh, household head; ENFR, Encuesta Nacional Factores
de Riesgo; ENPreCoSP, Encuesta Nacional sobre Prevalencias
de Consumo de Sustancias Psicoactivas.
*, **, and ***, significant at 10%, 5% and 1%, espectively.
TABLE 5
Discrete-Time Complementary loglog (cloglog)
Duration Models of Smoking Initiation,
16-33 Years Old Cohort Sample
Model Variables (1) (2)
Prices (in In)
Tobacco, sf -0.589 *** -0.400 ***
(0.121) (0.155)
Alcohol, sf 0.843 *** 0.838 ***
(0.179) (0.179)
Inflation (ref, no inflation)
Very high inflation 0.024 0.027
(0.060) (0.060)
Hyperinflation
Sex (ref, male) -0.337 *** -0.180 **
(0.028) (0.084)
Education, hh head (ref, primary)
Secondary 0.042 0.042
>Secondary (0.033) (0.033)
-0.027 -0.027
Tobacco control policies (0.036) (0.036)
Law 23.344, May 1986 -0.081 -0.082
Smokefree policies (0.224) (0.224)
-0.139 ** -0.140 **
(0.066) (0.066)
Province (ref, Buenos Aires city)
Buenos Aires, prov -0.035 -0.035
Catamarca (0.054) (0.054)
-0.288 *** -0.288 ***
Cordoba (0.063) (0.063)
-0.090 -0.088
Corrientes (0.063) (0.063)
-0.335 *** -0.335 ***
Chaco (0.064) (0.064)
-0.210 *** -0.210 ***
Chubut (0.062) (0.062)
0.166 *** 0.166 ***
Entre Rios (0.061) (0.061)
-0.113 * -0.112 *
Formosa (0.064) (0.064)
-0.609 *** -0.609 ***
Jujuy (0.071) (0.071)
-0.390 *** -0.390 ***
La Pampa (0.066) (0.066)
0.166 *** 0.166 ***
La Rioja (0.063) (0.063)
-0.135 ** -0.136 **
Mendoza (0.059) (0.059)
0.070 0.071
Misiones (0.061) (0.061)
-0.300 *** -0.301 ***
Neuquen (0.065) (0.065)
0.078 0.078
Rio Negro (0.062) (0.062)
0.160 *** 0.160 ***
Salta (0.061) (0.061)
-0.155 ** -0.155 **
San Juan (0.062) (0.062)
-0.017 -0.016
San Luis (0.062) (0.062)
0.080 0.080
Santa Cruz (0.059) (0.059)
0.305 *** 0.305 ***
Santa Fe (0.058) (0.058)
0.030 0.030
(0.062) (0.062)
Santiago del Estero -0.288 *** -0.288 ***
(0.065) (0.065)
Tucuman -0.011 -0.011
(0.061) (0.061)
Tierra del Fuego 0.262 *** 0.262 ***
(0.060) (0.060)
Survey/cycle
(ref, ENPreCoSP, 2011)
ENFR 2005 0.146 *** 0.146 ***
(0.043) (0.043)
ENPreCoSP, 2008 -0.003 -0.003
(0.042) (0.042)
ENFR, 2009 0.055 0.056
(0.036) (0.036)
Duration dependency
t 0.180 *** 0.180 ***
(0.005) (0.005)
t^2 -0.001 *** -0.001 ***
(0.000) (0.000)
t^3 0.000 *** 0.000 ***
(0.000) (0.000)
Interactions
Tob. price x sex -0.431 *
(0.223)
Tob. price x secondary
Tob. price x > secondary
Tob. price x very high inflation
Tob. price x very hyper inflation
Intercept -16.323 *** -16.370 ***
(0.849) (0.848)
Own-price elasticities
for tobacco
All -0.589 ***
(0.121)
Sex: male -0.400 ***
(0.155)
Sex: female -0.830 ***
(0.174)
hh education: primary
hh education: secondary
hh education: > secondary
Hyper, very high inflation: yes
Hyper, very high inflation: no
Model Variables (3) (4)
Prices (in In)
Tobacco, sf -0.337 * -0.751 ***
(0.199) (0.136)
Alcohol, sf 0.839 *** 0.876 ***
(0.179) (0.178)
Inflation (ref, no inflation)
Very high inflation 0.026 -0.562 ***
(0.060) (0.160)
Hyperinflation
Sex (ref, male) -0.337 *** -0.338 ***
(0.028) (0.028)
Education, hh head (ref, primary)
Secondary 0.175 * 0.041
>Secondary (0.098) (0.033)
0.168 -0.028
Tobacco control policies (0.104) (0.036)
Law 23.344, May 1986 -0.083 0.090
Smokefree policies (0.224) (0.233)
-0.138 ** -0.116 *
(0.066) (0.067)
Province (ref, Buenos Aires city)
Buenos Aires, prov -0.034 -0.034
Catamarca (0.054) (0.054)
-0.287 *** -0.287 ***
Cordoba (0.063) (0.063)
-0.090 -0.096
Corrientes (0.063) (0.063)
-0.335 *** -0.335 ***
Chaco (0.064) (0.064)
-0.211 *** -0.209 ***
Chubut (0.062) (0.062)
0.166 *** 0.166 ***
Entre Rios (0.062) (0.062)
-0.112 * -0.113 *
Formosa (0.064) (0.064)
-0.609 *** -0.607 ***
Jujuy (0.071) (0.071)
-0.389 *** -0.389 ***
La Pampa (0.066) (0.066)
0.166 *** 0.167 ***
La Rioja (0.063) (0.063)
-0.135 ** -0.135 **
Mendoza (0.059) (0.059)
0.070 0.071
Misiones (0.061) (0.061)
-0.301 *** -0.298 ***
Neuquen (0.065) (0.065)
0.077 0.077
Rio Negro (0.062) (0.062)
0.160 *** 0.160 ***
Salta (0.061) (0.061)
-0.156 ** -0.155 **
San Juan (0.062) (0.062)
-0.017 -0.020
San Luis (0.062) (0.062)
0.080 0.080
Santa Cruz (0.059) (0.059)
0.306 *** 0.306 ***
Santa Fe (0.058) (0.058)
0.030 0.028
(0.062) (0.062)
Santiago del Estero -0.287 *** -0.288 ***
(0.065) (0.065)
Tucuman -0.011 -0.015
(0.061) (0.061)
Tierra del Fuego 0.262 *** 0.263 ***
(0.060) (0.060)
Survey/cycle
(ref, ENPreCoSP, 2011)
ENFR 2005 0.148 *** 0.135 ***
(0.043) (0.043)
ENPreCoSP, 2008 -0.003 -0.007
(0.042) (0.042)
ENFR, 2009 0.055 0.052
(0.036) (0.036)
Duration dependency
t 0.180 *** 0.180 ***
(0.005) (0.005)
t^2 -0.001 *** -0.001 ***
(0.000) (0.000)
t^3 0.000 *** 0.000 ***
(0.000) (0.000)
Interactions
Tob. price x sex
Tob. price x secondary -0.360
(0.258)
Tob. price x > secondary -0. 533 *
(0.272)
Tob. price x very high inflation 1.163 ***
(0.303)
Tob. price x very hyper inflation
Intercept -16.394 *** -16.426 ***
(0.853) (0.844)
Own-price elasticities
for tobacco
All
Sex: male
Sex: female
hh education: primary -0.336 *
(0.199)
hh education: secondary -0.696 ***
(0.177)
hh education: > secondary -0.868 ***
(0.199)
Hyper, very high inflation: yes 0.411
(0.270)
Hyper, very high inflation: no -0.700 ***
(0.136)
Model Variables (5)
Prices (in In)
Tobacco, sf -0.611 ***
(0.120)
Alcohol, sf 0.849 ***
(0.179)
Inflation (ref, no inflation)
Very high inflation
Hyperinflation -0.364 *
(0.191)
Sex (ref, male) -0.337 ***
(0.028)
Education, hh head (ref, primary)
Secondary 0.043
>Secondary (0.033)
-0.027
Tobacco control policies (0.036)
Law 23.344, May 1986 -0.064
Smokefree policies (0.220)
-0.136 **
(0.066)
Province (ref, Buenos Aires city)
Buenos Aires, prov -0.034
Catamarca (0.054)
-0.288 ***
Cordoba (0.063)
-0.091
Corrientes (0.063)
-0.336 ***
Chaco (0.064)
-0.210 ***
Chubut (0.062)
0.166 ***
Entre Rios (0.062)
-0.113 *
Formosa (0.064)
-0.608 ***
Jujuy (0.071)
-0.390 ***
La Pampa (0.066)
0.167 ***
La Rioja (0.063)
-0.136 **
Mendoza (0.059)
0.070
Misiones (0.061)
-0.300 ***
Neuquen (0.065)
0.078
Rio Negro (0.062)
0.160 ***
Salta (0.061)
-0.155 **
San Juan (0.062)
-0.017
San Luis (0.062)
0.080
Santa Cruz (0.059)
0.305 ***
Santa Fe (0.058)
0.029
(0.062)
Santiago del Estero -0.288 ***
(0.065)
Tucuman -0.012
(0.061)
Tierra del Fuego 0.262 ***
(0.060)
Survey/cycle
(ref, ENPreCoSP, 2011)
ENFR 2005 0.150 ***
(0.041)
ENPreCoSP, 2008 -0.002
(0.041)
ENFR, 2009 0.055
(0.036)
Duration dependency
t 0.180 ***
(0.005)
t^2 -0.001 ***
(0.000)
t^3 0.000 ***
(0.000)
Interactions
Tob. price x sex
Tob. price x secondary
Tob. price x > secondary
Tob. price x very high inflation
Tob. price x very hyper inflation 0.696 **
(0.333)
Intercept -16.332 ***
(0.849)
Own-price elasticities
for tobacco
All
Sex: male
Sex: female
hh education: primary
hh education: secondary
hh education: > secondary
Hyper, very high inflation: yes -0.085
(0.310)
Hyper, very high inflation: no -0.610 ***
(0.102)
Notes: Robust standard errors in parentheses; hyperinflation
defined using Cagan's definition (May 1989 to March 1990);
very high inflation defined using Fisher et al.'s definition
(January 1980 to October 1991); hh, household head; ENFR,
Encuesta Nacional Factores de Riesgo; ENPreCoSP, Encuesta
Nacional sobre Prevalencias de Consumo de Sustancias
Psicoactivas. Number of observations, 8,414,893; number
of failures, 21, 414.
*, **, and ***, significant at 10%, 5%, and 1%, respectively.
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