Dans la pharmacie en ligne Viagra-représenté Paris large éventail de la dysfonction érectile anti-plus consommée. Générique Levitra (vardenafil), Cialis (tadalafil) et achat viagra pour homme, dont le prix est acceptable pour tous les budgets.1

En internet farmacia empecé a pedir porque en la farmacia de al lado nunca había deseado surtido de medicamentos comprar cialis Muy cómodo en el uso de la farmacia. Estuvimos en el restaurante a. aquí la tableta con la entrega en el lugar de.

## Wpielde n°

INSTITUTO DE ESTUDIOS LABORALES Y DEL DESARROLLO ECONÓMICO (ielde)
Facultad de Ciencias Económicas, Jurídicas y Sociales
Universidad Nacional de Salta (UNSa)

**Documentos de Trabajo **
**Elasticity of cigarette demand in Argentina: **
**An empirical analysis using **
**vector error-correction model*** *
Eugenio Martínez
Eliseo Pérez Estable
Noviembre de 2008
ielde

** – Facultad de Ciencias Económicas, Jurídicas y Sociales - UNSa **
UNSa: Av. Bolivia 5150, A4408FVY, Salta, Argentina
ISSN 1852-1118 (impreso), ISSN 1852-1223 (en línea)
Editor: Jorge A. Paz ielde@unsa.edu.ar

**Elasticity of cigarette demand in Argentina: **
**An empirical analysis using vector error-correction model*** *
Eugenio Martínez
Eliseo Pérez Stable

**Abstract **
** **

Objective: To estimate empirically the short and long-term effects on cigarette demand in Argentina

based on changes in cigarette price and income.

**Method: **We analyzed data from the Ministry of Economy and Production of Argentina. Analysis was

based on monthly time-series data between 1994 and 2004. The econometrics specification is a linear
double-logarithmic form using cigarettes consumption per person older than 14 y. as dependent
variable and real income per person older than 14 y. and the real average price of cigarettes sales as
independent variables. Empirical analyses were done in three steps: 1) To verify the order of
integration of the variables using the augmented Dickey-Fuller test; 2) To test for co-integration using
the Johansen-Juselius maximum likelihood approach to capture the long-term effects; and 3) To utilize
the Vector error-correction model to capture the short-run dynamics of the variables.

**Results:** The empirical results showed that in the long-term period the demand for cigarettes in

Argentina is affected by changes in real income and real average price of cigarettes. The value of
income elasticity is equal to 0.54 while the value of own-price elasticity is equal to –0.34.
The results using vector error-correction model estimation suggest that the short-term cigarette
demand in Argentina is independent of price (not statistically significant). The value of the short-term
income elasticity is equal to 0.49.
A simulation exercise show that increasing the prices in a 120% we can obtain a maximum of
revenues from cigarette tax and obtain also a big impact in the fall of the total consumption of
cigarettes in the country.
Key Words: Price elasticity, cigarette demand, Tobacco control
JEL Classification: D12, I18.

**Elasticity of cigarette demand in Argentina: **
**An empirical analysis using vector error-correction model.1*** *
**Eugenio Martínez **
Instituto de Estudios Laborales y del Desarrollo Económico-(IELDE). Facultad de Ciencias Económicas, Universidad Nacional de Salta, Salta, Argentina

**Raul Mejía **
Centro de Estudios de Estado y Sociedad and Programa de Medicina Interna General, Universidad de Buenos Aires, Buenos Aires, Argentina

**Eliseo J. Pérez Stable **
Division of General Internal Medicine, Department of Medicine, Medical Effectiveness Research Center for Diverse Populations, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, USA.
Empirical studies of cigarette demand have received considerable attention in recent years.
Many studies have examined the demand for cigarettes mainly in developed economies and
the number of studies focused on low- and middle-income countries is relatively limited. (Jha
and Chaloupka, 2000).
This interest is mainly due to the fact that the price and income elasticity of cigarette demand
are important for assessing proposals to revise cigarette tax, anti-smoking regulation and for
predicting the cigarette demand in future periods.
The issue of analyzing and predicting the evolution of cigarette demand is crucial for an
effective tobacco control policy though it is a complex topic. This paper approaches the
problem from an economic point of view and it is timely with respect to the new world trends
on the evaluation and elaboration of anti-smoking policies. Despite the importance of the
1 This study was funded by grant Nº TW05935 from the Tobacco Research Network Program, Fogarty International
Center, National Institute on Drug Abuse, National Institutes of Health, USA. We thank Teh-wei Hu, PhD for helpful
comments on an earlier draft and the data analysis and Cecilia Populus-Eudave for administrative and research
support at UCSF. The views expressed in this paper are solely those of the authors and do not necessarily reflect the
views of the institutions.
subject in the development of tobacco control policy, only one study has been done on
cigarette demand in Argentina.
In order to estimate long and short-run demand equations, the researchers used data with
different frequencies (e.g., annual, monthly) (see Agelike and Kostas 2001; Keeler, Hu,
Barnett and Manning 1993) for several countries (e.g., Greece, USA and others). Kim and
Seldon (2004) used econometric models in order to estimate the cigarette demand in the
Republic of Korea, and analyzed various government policies to control cigarette
consumption. They estimated the long and short-run price elasticities for the period 1960-
1997 with values of -0.35 and -0.27 respectively.
Valdes (1993) used a different approach called the "habit-persistent" model and estimated the
main determinants of cigarette demand in Spain from 1946 to 1988. This study employed a
partial adjustment model and used annual time series and found that cigarette demand in
Spain appeared to have similar values for the price elasticities for the short and long run (–
0.60 and –0.69 respectively).
Gallet and Agarwal (1993) applied an alternative method in order to estimate the specific
factors that affect cigarette demand in the US such as price and health information. These
authors used annual data for the period 1955–1990 to estimate a gradual switching regression
model and found that cigarette demand was negatively affected by changes in the price but in
a decreasing way throughout the time period. The elasticity price was –2.371 in the first
decade of the period under study and –0.140 in the last decade, but cigarette demand was
positively affected by the advertising with an elasticity that ranged between +0.65 to +0.008.
Baltagi and Levin (1992) employed panel data from 46 US States over the period 1963 to
1988 in order to capture the "bootlegging effect". In light of the results, their mainly findings
are a significant habit persistence effect, "border purchasing" effect and an inelastic own-price
effect. Another interesting approach to examine the main determinants of cigarette demand is
the "rational addiction model" proposed by Becker and Murphy (1988) which has mainly
been used to analyze cigarette consumption by Cameron (1999), Becker, Grossman and
Murphy (1994) and Chaloupka (1991), but also has been applied to estimate the demand of
other addictive goods such as opium by vanOurs (1995), alcohol by Chaloupka, Saffer and
Grossman (1993), cocaine by Grossman and Chaloupka (1998) and coffee by Olekalns and
Bardsley (1996). All these studies report negative and significant price effects, positive and
significant past and future consumption effects, and larger long run rather than short-run own-
price elasticity, (Grossman and Chaloupka,1998).
The study by Tiezzi (2005), estimated tobacco demand in Italy applying the rational addiction
framework, using first a pseudo-panel data and second time series data. Their results showed
that announcement of future price increases may be effective in curbing cigarette demand.
The only analysis of the cigarette consumption in Argentina was the study of Gonzalez
Rozada (2004). This study examined the demand for cigarette consumption in Argentina
employing double-log function model and used monthly data to explore the dynamic
relationships for cigarette consumption. The main results show a significant long run price
elasticity of –0.414. The cigarette consumption in Argentina is elevated and is not uniformed;
the tendency was decreasing from 1994 but demonstrated a change of direction during the last
year of the analysis. This pattern may be due to the absence of tobacco control policies and
to the low level of knowledge about the health risks attributable to smoking in Argentina.
Tobacco control advocates are currently attempting to pursue a mixture of reforms and
policies that include to reduction in overall consumption, increase in taxes, prohibiting the
consumption in public places, prohibiting the sale to minors and restricting tobacco
The purpose of the paper is to conduct an empirical analysis of cigarette demand in Argentina
over the period 1994 – 2004 using monthly data. Income and price elasticity of both the long-
and the short-run demand for cigarette use are examined in a multivariate framework. The
paper briefly describes the tobacco sector in Argentina, deals with methodological issues and
the data used in the empirical analysis, presents the empirical results and the policy
implications are discussed.

**Stylized facts for the Tobacco Market in Argentina.2**
Argentina is in the leading 12 tobacco growing countries in the world and second in Latin
America after Brazil (Mackay and Eriksen, 2002). Argentina produced about 95,000 tons of
tobacco leaves in 1990 and increased to a record volume of 157.300 tons in 2004. This
production is concentrated in seven provinces of northern Argentina and of these, three
provinces, Salta, Jujuy and Misiones produced 88% of the total of tobacco in the country.
The increase in tobacco production was accompanied by an increase in the total harvested
area, which changed from 57,750 hectares in 1990 to 77,600 hectares in 2004 or an increase
of 34%. The economic activity of tobacco farming and production crops is labor–intensive
and generates almost 60.000 jobs as direct work.3 Argentina is a net exporter of tobacco with
60% of the tobacco produced in the country is exported.
The tobacco industry in Argentina is led by two producers companies subsidiaries of
multinationals, Massalin Particulares S.A. of Phillips Morris Co and Nobleza Picardo of
British American Tobacco (BAT). Massalin Particulares has 60% of the national cigarette
market in Argentina. Given the structure of this market, the cigarette industry can be
classified as oligopolic in the output Market and like oligopsonic in the input market,
(Gonzalez Rozada, 2004). One characteristic to point out is that the tobacco production in
2 The data used in this section came from the Secretary of Agriculture, Livestock, Fish and Food-Department of Agricultural Economics. 3 We calculated this value following the methodology developed by Corradini, et.al. (2005).
Argentina is subsidized.
Figure 1: Avergae monthly consumption of cigarettes
Argentina 1994:1-2004:12
kcaP 1994m1 1995m1 1996m1 1997m1 1998m1 1999m1 2000m1 2001m1 2002m1 2003m1 2004m1
Figure 2: Average monthly real retail price of cigarette packs
Argentina 1994:1-2004:12
1994m1 1995m1 1996m1 1997m1 1998m1 1999m1 2000m1 2001m1 2002m1 2003m1 2004m1
This subsidy is paid to the producer as an over-price on the final cost of storing. In order to
finance this over-price the National Government collects the Special Tobacco Fund (FET)
through a specific tax on consumption of 7%. About 80% of this fund is distributed to the
producers trough the subsidy previously described.
The average real retail price per pack of cigarettes was stable between January of 1994 and
December of 1999 with a gap between maximum and minimum for that period of $ 0.17.
From that date the average real retail price per pack presented wide fluctuations reaching a
minimum of $ 1.27 in March of 20034.
Since that date the real price had an increasing tendency reaching a maximum in December of 4 Real retail price in 1993 pesos.
2004 ($2.02). The 69% of retail price is conformed by different type of taxes (indirect taxes,
VAT, etc), Ministry of Health and the Environment (2005). The monthly average
consumption of cigarettes in Argentina was of 160 million of packages for the period 1994 to
2004 (a monthly average of 6.11 packs by persons older than 14 years of age). Reaching a
maximum of approximately 8 packs per person older than 14 years of age in December 1999.
As is well known the economic activity previously described and therefore its final product
"cigarettes" is highly addictive and its consumption has serious adverse effects on health. In
Argentina, the prevalence rate for people of 13 to 64 years old and living in the main urban
centers of the country was 32.7% in year 2004, Ministry of Health and the Environment
The total smoking prevalence in Argentina was 38.3% for men and 24.5% for women in 2001
(Martinez, Kaplan, Guil, Gregorich, Mejia and Perez-Stable, 2006). Conte Grand (2005)
estimated for year 2003 that the deaths attributable to the tobacco consumption in Argentina
were of 41,280 people older than 35 years old, which generated a cost by lost of future
earnings by premature death of $2.315 million (pesos of 2003).

**Methodological Framework and Database. **
Following the specification of Gonzalez Rozada (2004), a linear double-logarithmic form
using income and price as independent variables was used in the empirical analysis.
Therefore, in the empirical study the following specification for the long-run demand for
cigarette was employed:
ln(

*Qpc *) = α + α ln(

*RYpc *) + α ln(

*RP *) + α

*D *+ µ (1)
where

*Qpc * is the per capita consumption for cigarette at time t,

*RYpc * is real per capita
income at time t in pesos in 1993 prices,

*RP * is the real average price of cigarettes,

*D *is some
seasonal dummy variable and µ is an error term.

*Qpc *is the quantity of cigarettes consumed
and was measured as numbers of cigarettes per person older than 14 years old;

*RYpc * is

* *the
real income measured as the real gross domestic product (GDP) in real terms per capita.
This analysis was carried out using the available data from Argentina; it was for the period
1994:1– 2004:12. The variables were not seasonally adjusted5. All data except population
data were obtained from the Ministry of Economics and Production in Argentina. The
population data were collected from the INDEC-National Institute of Statistics and Census-
(2004). The data corresponding to the GDP were generated on a quarterly frequency but in
order to adjust to the model using monthly frequency, the Chow-Lin procedure (1971) was
carried out to obtain monthly series from quarterly frequencies6.

**Table1. **Descriptive Statistics of Data

**Variable **
**Cigarette per person > 14 years **
**old **

Packs per person >14 years old
**Real retail price **
**Real income per capita **
On the other hand, the information referring to the population greater than 14 years old was
available only on an annual frequency and thus was made into an interpolation in a constant
growth rate to obtain monthly series. In the empirical analysis, we tested for the existence of
a long-run relationship among the variables (estimation of Eq. (1)) while the utilization of the
vector error-correction model captures the short-run dynamics of the variables. The analysis
was done in two steps and the initial one is to verify the order of integration of the variables
since the various co-integration tests are valid only if the variables have the same order of
5 Ghysels and Perron (1993) showed that it is better to work with seasonally unadjusted data when the Augmented Dickey-Fuller (ADF) test will be used. Due to the fact that if filtered data are used; the test ADF will be biased toward non rejection of the unit root null hypothesis. 6 For this procedure was used like a related series: the Monthly Estimator of Economic Activity of Argentina (EMAE) from National Institute of Statistics and Census (INDEC).
integration. Standard test for the presence of a unit root based on the work of Dickey and
Fuller (1979, 1981) (ADF) was used to investigate the degree of integration of the variables
used in the empirical analysis. The second step involved testing for co-integration (Eq. (1))
using the Johansen maximum likelihood approach, Johansen (1988) and Johansen and
Juselius (1990, 1992).
The Johansen–Juselius estimation method is based on the error-correction representation of
the Vector Autoregressive (VAR) model with Gaussian errors. The presence of evidence of
co-integration rules out the possibility that the estimated relationship is spurious.
Engle and Granger (1987) showed that in the presence of co-integration there always exists a
corresponding error correction representation, which implies that changes in the dependent
variable are, a function of the level of disequilibrium in the co-integrating relationship,
captured by the error-correction term (ect), as well as changes in other explanatory variables
to capture all short-term relations among variables.
Campbell and Perron (1991) provide rules of thumb for investigating whether time series
contain unit roots. To begin, we estimated the following three forms of the augmented
Dickey–Fuller (ADF) test where each form differs in the assumed deterministic component(s)
∆ = δ

*x *+ ∑ ∆
φ

*x *− µ
1

*t * 1

*i* *t* *i* *t* (2)
∆

*x *= δ + δ

*x*
φ

*x *− µ (3)
∆ = δ +δ

*x *+
φ

*x *− µ (4)
where

*x *= {

*Qpc *,

*RYpc *,

*RP *. The µ

* *is assumed to be a Gaussian white noise random error
and

*Time*=1,…,

*T *(the number of observations in the sample) is a term for trend. In Eq. (2)
there is no constant or trend. Eq. (3) contains a constant but no trend. Both a constant and a
trend are included in Eq. (4). The number of lagged differences,

*P*, is chosen to ensure that the
estimated errors are not serially correlated.
The results from the unit root tests are shown in Table 1. The first three rows test the null
hypothesis that a series follows a unit root process or random walk. This implies it is non-
stationary and (possibly) integrated of order one,

*I*(1), rather than

*I*(0). The second three rows
test the null hypothesis that first difference of a series follows a unit root. If true, the
researcher must difference the series twice to obtain a stationary process.
We found that for all series in Table 1 the null hypothesis of a unit root in the level cannot be
rejected. There is evidence that cigarette consumption per capita is stationary,

*I*(0), for the
ADF regression including a constant and a constant plus trend term (Eqs. 3 and 4).
However, further testing suggested that the model without constant or trend was the
appropriate choice. The constant term and the slope coefficient of the trend term were
insignificant. The tests for unit roots in the second differences are rejected, implying that the
series is

*I*(1) and stationary in their first differences.

**Table 2. **ADF statistics testing for a unit root

**Augmented Dickey-Fuller **
**Variable **
∆

**LRYpc **
All variables are in natural logarithms. The first three rows present the ADF

*t*-tests
corresponding to tests for unit roots in the levels of the series. The last three rows report the
ADF

*t*-test results for testing whether the first difference has a unit root. A rejection implies
that the first difference of the series is a stationary process. The last three columns

* *refers to
Eqs. (2)–(4) in the paper, which are ADF regressions with no constant, a constant and a
constant plus trend, respectively. The critical values for the

*t*-tests at 5% are y -1.94, -2.88 and
-3.44, respectively; at 1% they are -2.58, -3.48 and -4.04, respectively. Rejections at the 5 and
1% critical values are denoted as * and **, respectively. The critical values for this table are
calculated from MacKinnon (1991). The lag length structure of φ of the dependent variable

*x * is determined using a recursive procedure in the light of a Lagrange multiplier (LM)
autocorrelation test (for orders up to 13), which is asymptotically distributed as chi-squared
distribution and the value of t-statistic of the coefficient associated with the last lag in the
estimated auto-regression.

**Co-integration Analysis and Long-Run Relationship. **
Co-integration tests are a multivariate form of integration analysis. Individual series may be

*I*(1), but a linear combination of the series may be

*I*(0). The error correction model is a
generalization from the traditional partial adjustment model and permits the estimation of
short-run and long run elasticity.
The approach is based on the findings of Nelson and Plosser (1982), in which many
macroeconomic and aggregate level series are shown to be well modeled as stochastic trends,
i.e. integrated of order one, or

*I*(1). Simple first differentiation of the data will remove the
non-stationary problem, but with a loss of generality regarding the long-run ‘equilibrium'
relationships among the variables. Engle and Granger (1987) solve this filtering problem with
the co-integration technique. They suggest that if all, or a subset of, the variables are

*I*(1),
there may exist a linear combination of the variables that is stationary,

*I*(0). The linear
combination is then taken to express a long-run ‘equilibrium' relationship. Series that are co-
integrated can always be represented in an error correction model. The error correction model
is specified in first differences, which are stationary, and represent the short-run movements
in the variables. When the error correction term (ect) is included in the model, the long run, or
equilibrium, relations are accounted for. The ect term represents the deviation from the
equilibrium relation in the previous period. Lags of the independent and dependent variables
would be included to capture additional short- and medium-term dynamics of cigarette
To determine the lag length of the VAR and co-integration analysis we used Hannan-Quinn
(HQIC) and the Bayesian Schwarz Information Criterion (BSIC). These measures compared
the fit of the maintained model against reductions in the number of explanatory and
predetermined variables. Given the monthly frequency of the data, an initial version of the
VAR with 12 lags was estimated. The results indicate an optimum length of 2 lags. The
estimated statistics, for the VAR = 2, indicate not only the absence of serial correlation but
also support the structural stability of all the estimated regressions.
Specifications of the VAR with smaller number of lags reveal serial correlation in the
estimated regressions. Thus, a VAR = 2 is employed in the estimation procedure of co-
integration. It was tested whether the estimated regression equations were stable throughout
the sample using the CUSUM and CUSUMSQ tests on structural stability of the estimated
relations. Finally, a log-likelihood ratio test is used for testing the deletion of three dummy
variables from the VAR model. The first dummy variable (Dummy 97) accounts from the
moment when was established that the cigarette sale was prohibited for persons under 18
years old (March 1997). The second dummy7 (D(ACS)) accounts for the increase of cigarette
consumption during Christmas holydays and the payment of the annual complementary salary
(with a value of 1 for December and 0 in all others months) and the last dummy (Dummy 02)
capture the moment when the macroeconomics policies changed (March 2002). All tests
reject the null hypothesis of the deletion of the first two dummy variables from the VAR
Table 3 contains the results of co-integration analysis among per capita cigarette
consumption, real income per capita and real price of cigarettes in order to estimate Eq. (1).
To test for co-integration, we use the Johansen-Joselius maximum likelihood approach
employing both the maximum eigenvalue and trace statistic. The results from the co-
integration test showed that both maximun eigenvalue and trace test statistics imply that there
was one co-integration vector among cigarette consumption, disposable income and price.

**Table 3. **Johansen-Juselius Cointegration Test

**Trace Statistics **
**Alternative **
**Critical Value **
**Maximun Eigenvalue Statistics **
**Alternative **
**Eigenvalue **
**Critical Value **
r indicates the number of cointegrating relationships.
The estimated lung-run demand is summarized in the equation:
ln(

*Qpc *) = 0.10 + 0.54 ln(

*RYpc *) − 0.34 ln(

*RP *) + 0.27

*D*(

*ACS*) (5)
(4.59) (-4.23) (5.36) 7 Several seasonal dummies were tried and the unique one that resulted to be statistically significant was the correspondent to December.
where (.) contains t-statistics. All two coefficients have significant correct signs. The long-run
elasticity of price and income are respectively

**VECM and Short-run Relationship. **
Having verified that a co-integrating relationship exists between the variables, the VECM can
be applied. The error-correction term measures the proportion by which the long-term
imbalance in the dependent variable is corrected in each short-run period. The size and the
statistical significance of the error-correction term measures the extent to which each
dependent variable has the tendency to return to its long-run equilibrium.

**Table 4. **Short-Run Relationship

**Variable **
**Coefficient **
∆ ln(

*RP*)
∆ ln(

*RP*)
∆ ln(

*Qpc*)
∆ ln(

*Qpc*)
∆ ln(

*RYpc*)

*Dummy*(

*ACS *)

*ect *(error correction

*R *= 0.72

*F *−

*statistic *= 33.70

*DW *−

*test *= 1.82

*ARCH *−

*test *= 0.74

*White *−

*heteroskedasticity *=0.60
In the restricted dynamic cigarette demand presented in Table 4, all the estimated coefficients,
including the error-correction term, are statistically significant and have a correct sign.
The error-correction term is equal to 0.78 suggesting that the speed of adjustment is equal to
78%8. Growth in cigarettes consumption 2 months before the current consumption has a
statistically significant negative effect. The estimated coefficient for the short-run change of
real income is positive and significant and its value is equal to 0.49. This value is
considerably closer to the long-run value and implies that a 10% increase in the growth of real
income will lead to an increase of cigarette consumption by 4.9% in the short run. The
estimated coefficient for the short-run effect of the price is not statistically significant.
With respect to the coefficient of the Dummy97 variable; which captures the effect to prohibit
the sale of cigarettes to persons under 18 years old, can be observed that the same one is
statistically significant and with negative sign.
The demand function for cigarette appears to be well specified since it passes a series of
diagnostic tests including the serial correlation, the autoregressive conditional
heteroskedasticity test (ARCH test) and the heteroskedasticity test.

**Table 5. **Summary of the Elasticities.

**Long-Run **
**Short-Run **
Price-Elasticity (η
Income-Elasticity (η

**Discussion and Policy Implications **
This paper examined the demand of cigarette in Argentina employing monthly data over the
period 1994–2004. Co-integration techniques were applied to estimate the demand and to
examine the issues of stability, income and price sensitivity of both long- and short-run
demand of cigarettes. Finally, the importance of short-run deviations was presented using
8 In table 4, only the restricted error-correction equation for cigarette demand is presented. All other equations are available from the authors upon request.
vector error-correction model estimation.
The empirical results suggest that in the long-run period the demand for cigarette is affected
by changes in real income and real price. The value of income elasticity was equal to 0,54
while the value of price elasticity was equal to -0,34. The results using error-correction model
estimation suggest that the short-run demand of cigarettes in Argentina is independent of price
and the value of income elasticity in the short-run is equal to 0,49.
The elasticity values obtained in this study provided valuable information for planning
tobacco control policies. Due to this potential utility we developed a simulation exercise
following the example by Hsieh (1998) to show the possible impact of increasing the final
price of cigarettes on consumption and on revenue from cigarette tax. The initial assumptions
or values for the simulation are those that are in the column "Status Quo" in table 6. The
values are the corresponding ones to the last quarter of the year 20049. The monetary values
are in pesos as of December 2004, the values corresponding to the consumption of cigarettes
and the revenue from cigarette tax were from the last quarter of 2004. The tax increases were
designed in a way that when the cost was completely transferred to the final retail prices and
thus reflects an increase of 10%, 20%, 30%, on this final price.
Table 6 only contains information about seven different increases of the cigarettes final price,
but the complete simulation reach until an increase of 290%, which can be observed in figure
9 We took a quarterly as long run because was captured the short run dynamic in VECM with 2 lags and we are working with monthly data.
Figure 3: Revenue from alternative rates of cigarette tax and total cigarette consumption
Price Increases (%)
Total Cigarette cosumption
Revenue from cigarette tax
From the simulation we can obtain important information for tobacco–control policies. An
increase in the final price of 20% can lower the total consumption of cigarettes packs in 34.70
million in a quarter and can also generate an increase in the fiscal revenue from cigarette tax
of $ 209,70 millions.
On the other hand a bigger increase of prices, for example of 50% in the final price, generated
a fall in the consumption of cigarettes per person > 14 years old of 3,08 packs quarterly and
an increase of $447,94 millions in the tax revenues.
If we observed the figure 2, is possible to see that in Argentina a wide margin exists to
increase the cigarettes prices without falling in lost of tax revenues. Increasing the prices in a
120% we can obtain a maximum of revenues from cigarette tax and obtain also a big impact
in the fall of the total consumption of cigarettes in the country (see the last column in table 6).

**Table 6. **Simulation of alternatives increase of cigarette retail price (Quarterly data).

**Long-run own price elasticity = -0.34 **
**Price increase **
**A- Average retail price **
**($) **

B- Average tax per pack
**($) **

C-Total cigarette

consumption (millions
**of packs) **

D- Changes in C
**(decrease) **

E- Cigarette

consumption per person
**>14 years old (packs) **

F- Changes in E
**(decrease) **

G- Revenue from

cigarette tax
1,127.47 1,227.24 1,316.83 1,396.24 1,465.48 1,524.55 1,665.28

**($ millions) **

H- Changes in G
Note: U$S 1 = $ 2.96 in December 2004.
The results and simulation suggest that increases in the cigarette prices (Tax) in Argentina,
can be an effective instrument for reduce the tobacco consumption only in the long run while
in the short run changes in prices will not alter the quantity of cigarettes consumed. In
addition, the high-income elasticity in the long run implies that a substantial higher cigarette
consumption pattern is expected as the real income of the Argentinean converges to the real
income of the households of the other countries in the developed world. Finally, Argentina is
currently working in different antismoking programs and policies and trying to implement the
Framework Convention from the WHO. Therefore, policy makers and tobacco control
advocates could benefit from the findings of this study that provides useful information on the
characteristics of the market for cigarette consumption and may help to plan their strategy.

**References **
Agelike, N. & Kostas, V., (2001). Estimating the demand for cigarettes in Greece: an error
correction model.

*Agricultural Economic Review*, Vol. 2(1), p. 20-27.
Baltagi, B. & Levin, D. (1992) Cigarette Taxation: Raising Revenues and Reducing
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