Addiction and the Demand for Alcoholic Beverages:Evidence from Italian DataPierpaolo Pieraniand Silvia TiezziDepartment of Economics - University of SienaOctober 14, 2004AbstractIn this paper we estimate the demand for alcoholic beverages in Italy following therational addiction demand framework by Becker and Murphy (1988) and using the GMMestimator. We use time series of national accounts data on an aggregate of alcoholicbeverages expenditures from 1960 to 2002 supplied by ISTAT. We take into account Auldand Grootendorst (2004) recommendations when estimating the rational addiction modelusing aggregate time series. The data seem to t the model quite well implying thatalcohol consumers are actually forward looking. We also get a reasonable estimate ofthe intertemporal rate of time preference at the sample mean. Short and long run priceelasticities of demand as well as the income elasticity of demand are also calculated. Thelong run elasticity is higher than the short run one, but the demand is rather unelastic.Surprisingly, the income elasticity of demand for alcohol, as derived from the rationaladdiction model, is higher than one both in the short and in the long run so that alcoholicbeverages turn out to be luxury goods.Keywords: rational addiction; alcohol consumption;JEL Classication D12, C23Acknowledgments:We would like to thank, without implicating, Pier Luigi Rizzi for his insightful com-ments and critiques on earlier versions of the paper.Corresponding author: Pierpaolo Pierani, Dipartimento di Economia Politica, Piazza San Francesco, 7,Siena, Italy. E-mail: pierani@unisi.it.11 IntroductionThe consumption of alcoholic beverages has features and consequences that make this classof commodities dierent from traditional consumption goods. As Cook and Moore (2000,p. 1635) have emphasised, alcohol is an intoxicant: consumed in sucient quantity in asingle session it impairs mental and physical functioning and it is potentially toxic. Secondly,alcohol consumption has direct intertemporal consequences: past consumption generates habitformation and addiction. Thirdly, chronic alcohol use aects physical and mental health overthe course of years or decades. While the second and third consequences are also typicalof other addictive goods, such as cigarettes for instance, the rst one is peculiar of alcoholconsumption and generates important social costs.In this paper we estimate the demand for alcoholic beverages in Italy testing the rationaladdiction model developed by Becker and Murphy in 1988. Its estimable version1has pro-duced a large bulk of empirical literature, because of its relevant policy implications. Morespecically, the model implies that the demand for addictive goods is quite sensitive to pricechanges in the long run. Therefore, contrary to common sense beliefs, price changes could beeective measures of curbing alcohol consumption, for instance.Our study is primarily concerned with contributing towards this strand of literature as wellas investigating the demand for alcoholic beverages in Italy. To our knowledge, only very fewexamples exist that confront the rational addiction hypothesis with Italian data and this paperis the rst attempt at providing such an evidence on alcohol consumption. The investigationperiod covers the years 1960 - 2002. We use time series on an aggregate of alcoholic beveragesexpenditures and estimate, with the GMM, a structural rational addiction demand equationas well as a myopic demand function.The paper is structured as follows. An overview of alcohol consumption and alcohol policies,both in Italy and in Europe, is given in section 2. In section 3 we briey review the analyticalframework and the existing empirical evidence on rational addiction to alcohol. Section 4explains the empirical methodology and shows the results. Section 5 concludes.1The estimable version of the Rational Addiction Model has recently been criticised by Wangen (2004) whohas stressed how the estimable structural rational addiction equation is valid only provided that some ratherstrong assumptions hold.22 Alcohol Consumption and Policies2.1 Alcohol Policies in the European RegionSince 1992 the World Health Organization (WHO) has advocated a combined approach toreduce harm resulting from the use of alcohol, tobacco and drugs. To this aim, the secondEuropean Alcohol Action Plan (EAAP) for the years 2000 - 2005 has been launched by theWHO Regional Committee for Europe at its forty-ninth session in 1999. This is a rst attemptto establish a regional policy framework, a set of guidelines to which member states havecommitted themselves. In February 2001 a joint conference WHO - European Union (EU)has put forth a set of strategies aimed at reducing alcohol related harm among the young.The objectives set forth in the Declaration on Young People and Alcohol are to be reachedby 2006. The debate that has followed in the EU has produced a Recommendation of theCouncil of Europe (Drinking of alcohol by Young People, in particular children and adolescents,2001/458/EC) and the following Conclusion (Council Conclusion on a Community strategyto reduce alcohol-related harm 2001/C 175/01). A further contribution of the EU is given bythe new Public Health Project Community Action in the Field of Public Health 2003 - 2008recently adopted by the European Parlamient (Decision of the European Parlamient and ofthe Council n. 1786/2002/EC) that identies, among others, the main initiatives to be takenwithin the EU to modify individual behaviours related to alcohol, drugs, food and cigarettesconsumption.The European Region of the WHO includes 51 countries2and about 870 millions of people.Within this region, alcohol products are estimated to be responsible for about 9% of thetotal disease burden (Rehn, Room and Edwards, 2001, p.21).They are linked to accidentsand violence and are responsible for a large proportion of the reduced life expectancy in thecountries of the former Soviet Union. Estimating the cost of alcohol consumption is verydicult because of the problem in calculating the costs of unhappiness, pain, general ill healththat may be somehow linked to alcohol consumption. For this reason alcohol costs are likelyto be under estimated. Most often the social costs included in the calculations are: workplaceproduction losses, the costs of accidents, health care expenditures, the costs of treatment,social welfare payments and social community costs (crime, enforcement and penal costs). Itis estimated that the total net (balanced against income from alcohol) social costs of alcoholin the European region amount to between 1 and 3% of gross domestic product (Klingemann,2001, p. 9). A rst dierence between the social costs of smoking and the social costs of2The Russian Federation, most of the eastern european countries including the countries of the formerSoviet Union, Israel and the European Union.3drinking can be traced by noticing that smoking does not give rise to costs of accidentsand the social community costs mentioned above. These are more properly linked to theconsequences of binge drinking. Based only on the recorded level of alcohol consumption for1998, the WHO Regional Oce for Europe has divided the Region into countries with a highlevel of consumption (more than 10 litres of pure alcohol per person per year), middle levelof consumption (more than ve litres per person per year) and low level of consumption (lessthan ve litres per person per year) with Italy belonging to the second group3.Alcohol policy in the enlarged European Region is implemented through dierent strate-gies: information and education; restrictions of alcohol consumption in dierent settings; drinkdriving campaigns; restrictions on the availability of alcohol products; promotion and adver-tising; treatment of alcohol related health problems; taxation specically aimed at reducingalcohol consumption. The EU has adopted directives that relate to three aspects of alcoholcontrol policy: taxation, advertising and transportation between member states of alcoholfor personale use. The EU has also attempted to harmonize taxation on alcohol among itsmember countries, but to date it has succeeded only in setting minimum excise rates4.TheEU has also placed restrictions on the advertising of alcohol on television5.2.2 Alcohol consumption and policy in Italy2.2.1 Alcohol ConsumptionIn the year 2000 the average per capita consumption of pure alcohol in Italy has been about7.5 litres (Ministero della Salute, 2003, p. 12), but according to the WHO for the EuropeanRegion, the target of 6 litres per capita per year should be reached by the year 2015.Alcohol consumption has followed a decreasing trend since the early eighties. If we lookat aggregate consumption of alcoholic beverages over time in Italy, the per capita level hasdecreased, between 1970 and 2001, by 51.25%, as can be seen in gure 1.3Between 1988 and 1998 Bulgaria, Estonia, Italy, Switzerland and Ukraine have achieved a rather largedecrease in the recorded level of consumption, although Estonia and Ukraine have a high level of unrecordedconsumption (Rehn, Room and Edwards, 2001, p. 9). During the same decade, however, a number of countrieshas shown a considerable increase in the recorded level of alcohol consumption: Turkey, the Russian Federation,Belarus, Ireland, Latvia and the Czech Republic. Countries with a relatively stable level of alcohol consumptioninclude Denmark, Finland, Germany, Iceland, the Netherlands, Norway, Slovenia and the United Kingdom.4According to the Council Directive 92/83/EEC of 12 October 1992, the minimum rates, from January1993, are as follows: for wine (still and sparkling): ECU 0; for beer: ECU 0,748 per hectolitre per degree Platoor ECU 1,87 per hectolitre per degree of alcohol of the nished products; for intermediate products (beverageswith an alcohol content under 22% and not belonging to the group of wines and beers): ECU 45 per hectolitreof product; for spirits: ECU 550 per hectolitre of pure alcohol (Rehn, Room and Edwards, 2001, p. 68).5The Council Directive 89/552/EC of 3 October 1989 restricts the content of alcohol advertisements on TV.41970 1975 1980 1985 1990 1995 20008910111213141516Figure 1: Pure Alcohol Consumption: Litres per-capitaSource: Health for All Database, 2003.These kind of data, however, present a number of drawbacks. On one hand, aggregate datamay be dominated by the behaviour of light and moderate drinkers and a decrease in aggregateconsumption could hide a rather dierent picture. For this reason, the use of micro-data isrecommended for this kind of studies. On the other hand, addictive behaviour, for instance,could be more easily captured by data on spirits consumption only, because the distribution ofspirits is tipically more bimodal (as predicted by the Becker and Murphys model of addiction)than that of wine and beer. Aggregate data separated for wine, beer and spirits are available,for Italy, from 1981 to 2000. Table 2, taken from the Italian Institute of Health (Scafato andRusso, 2004, p. 4), shows that the total per capita decrease in alcohol consumption from1981 to 2000 results from a 40.8% decrease in wine consumption; a 65.7 % decrease in spiritsconsumption and a 57% increase in beer consumption.A rather dierent pattern of consumption emerges from the study of Bentzen, Erikssonand Smith (1999) on alcohol consumption in four northern European countries: Denmark,Sweden, Finland and Norway. Here the consumption of spirits has almost halved from 1960 to1992 in the four countries. Beer and wine consumption have increased in Finland, Norway andSweden, whereas beer consumption has decreased in Denmark, where beer consumption hasalways been very high. Overall, the consumption of pure alcohol over time in the four nordic5countries shows an increasing trend, because the decrease in spirits consumption is more thancompensated by the increase in wine and beer consumption. Italy seems to show an oppositetrend: here the increase in beer consumption does not compensate for the decrease in wine andspirits consumption also because beer has the lowest alcohol content among the three types ofalcoholic beverages.Despite the obvious drawbacks in aggregate data, we could check whether this evolutionin alcohol consumption in Italy bears some relationship with the evolution of the real price,as would be expected when studying the consumption trend of a traditional commodity. Ingure 2 we have plotted an index (equal to 1 in 1995) of the real per capita (14 or older)expenditure on all alcoholic beverages and the real alcohol price over the years 1960 - 2002.A quick glance at the picture reveals that the consumption of alcoholic beverages does notseem alike that of traditional commodities. It is only from around 1977 onwards that therelationship between the real price and the real expenditure seems to be as expected: the realprice decreases sharply to reach a minimum in 1985, whereas real expenditure remains almoststable at 1977 levels. From 1985 onwards the price starts increasing again and, as expected, realconsumption decreases continuously to reach a minimum in 2002. The correlation coecient,calculated over the whole period, between these two variables is indeed rather low and equalto -0.29.The graph seems then to suggest that alcohol consumption before 1977 was drivenby variables other than the price, whereas later on the role of the real price has become morerelevant in explaining trends of real consumption. The correlation coecient, calculated overthis subperiod, is indeed equal to -0.54.61960 1965 1970 1975 1980 1985 1990 1995 20001.01.11.21.31.41.51.61.7ALCP Figure 2: Index (1995=1) of per capita alcohol expenditure and real alcohol price.Source: Italian National Accounts.In 2001 75% of Italians older than 14 has consumed alcoholic beverages: 87% of men and63% of females (table 1) (Scafato, Ghirini and Russo, 2004, p. 14).Survey data on life styles and health conditions of individuals are collected every two yearsby ISTAT, the Italian Statistical Oce, and may help understanding what are the variables,other than the price, driving alcohol consumption. These survey data, however, are availableonly for the years 1993 - 2001 (Indagine Multiscopo sulle famiglie - stili di vita e condizioni disalute).The observed sharp decrease in the per capita consumption has been accompanied, duringthe nineties, by a remarkable change in the drinking habits. This change is characterisedby three main facts: a) an increase in the number of female consumers; b) an increase inthe number of young consumers (teen agers and people aged 18 to 24); c) an increase in thenumber of people (and the increase is higher for females and the young) consuming alcoholoutside the main meals.The increase in the number of alcohol consumers on one hand and the sharp decrease inthe per capita level of consumption, on the other, seem to testify a change in the consumptionhabit. Italy is a wine producer country where, traditionally, wine has always and mainly beenconsumed, on average, in moderate quantities and by all members of the household, to accom-7pany meals. The changing pattern seems to suggest a transition from a Mediterranean modelof alcohol consumption to a drinking pattern closer to the northern european one characterisedby binge drinking and by the use of alcohol as a bridge to ease personal relationships and wanedown social discomfort or as a means of female emancipation and cultural homologation.Ifthis is true, then the comforting picture of a continuously decreasing level of alcohol consump-tion could hide an increase in the number of people actually at risk, especially among the mostfragile groups of the population.2.2.2 Alcohol PolicyItalys alcohol control policy seems to rely mainly on command and control policy options -such as restrictions and prohibitions - and on voluntary agreements. Preventive and treatmentstrategies are less developed whereas the use of economic policy instruments, such as taxesaimed at reducing alcohol consumption, is not contemplated.The majority of countries has a system of two taxes on alcoholic beverages: a value addedtax (VAT) and a specic alcohol tax. Italy has a VAT on alcohol (20%) higher than on otherproducts or services. The majority of the European countries have a VAT on alcohol closeto 20%.The specic alcohol tax is mostly based on the volume of alcohol: the stronger thebeverage, the higher the tax, but this is not the case in Italy. The ocial objective of alcoholtaxation in Italy is to raise revenues to nance public expenditure. Reducing total alcoholconsumption is not contemplated among the objectives of taxation. Moreover, Italy does nottax wine. To ensure the collection of taxes and to counteract smuggling duty paid stamps forspirits have been introduced.Promotion and advertising of all types of alcohol is restricted on TV, radio, print mediaand billboards with two exceptions: advertising of spirits on billboards and of beer on theradio is only subject to a voluntary code. A ne is imposed as a punishment if advertisingregulations are breached.The legal maximum blood alcohol concentration (BAC) in Italy is, since 2001, 50 mg%.The increased risk for young and professional drivers is recognised and a lower BAC limitfor those categories is required. Random Breath Testing (RBT) has been introduced. Thepolice needs justied suspicion to breath test drivers and the penalties for drink driving are asuspension of the driving licence from 15 days to 3 months, a ne and imprisonment of up toone month.As to strategies aimed at limiting the availability of alcohol products, Italy has a licensingsystem for import, export, production, wholesale and retail sale of all types of alcoholics. There8is a formal procedure to obtain a licence for retail outlets, but all applicants get a licence, thusthe procedure is almost meaningless. Restrictions on sale of alcoholic beverages relates only tosales at specic events: childrens festivals, sporting events and demonstrations. During theseevents, the restriction is partial for beer, voluntary for wine and total for spirits. There is anage limit of 16 for access to alcohol purchase.Between 1994 and 1999 Italy, together with other European countries, has introducedpolicy changes aimed at increasing alcohol consumption control. These changes have occurredin two main areas: drink driving legislation and alcohol promotion. Random Breath Testing(RBT) has been introduced to reinforce drink driving controls.However, the overall level ofenforcement of drink driving legislation is regarded as loose. To reduce alcohol promotion,Italy has introduced more restrictions on sale and alcohol advertising: the age limit of 16 hasbeen introduced for alcohol purchase and the hours of sales of spirits have been restricted.The most recent public measure concerning alcohol consumption is the law 125/2001 (LeggeQuadro in Materia di Alcohol e di Problemi Alcolcorrelati, 30.03.2001 n. 125). This lawintroduces measures aimed at the prevention and treatment of alcohol related problems in linewith the recommendations of the European Alcohol Action Plan.While these command and control strategies are important in controlling alcohol consump-tion and related problems, their monitoring and enforcement is the crucial key to determinetheir eectiveness. Moreover, the implementation of the law provisions requires an extra ex-penditure of 2.130 Millions Euro per year.The use of economic incentives, such as taxes, is not contemplated among the measuresthat may help reaching the goal of reducing alcohol related social costs.In this respect, theeconomic models of demand and, particularly, the addiction models, may help highlightingthe eectiveness of price mechanisms in reaching those targets.3 Analytical Framework and Empirical EvidenceThe usual way of modeling alcohol consumption is, nowadays, the rational addiction (RA)framework developed in the seminal paper by Becker and Murphy (1988). Previous empiricalresearch developed the notion of dependency via models of habit formation, otherwise knownas myopic model of addiction, or else, it ignored the addictive nature of alcohol and modeledalcohol demand as a traditional consumption good.Following Becker, Grossman and Murphy (1994) (BGM henceforth), we assume that theaddicted consumers problem is to maximize, over the life cycle, the following utility function:9V = Xt=1t1U (Yt, Ct, Ct1, et) (1)where Yt is consumption of a non addictive good (used as numeraire) at time t, Ct isconsumption of the addictive good, alcohol in this case, at time t, Ct1 is consumption ofalcohol in the previous time period,et reects unmeasured life cycle variables that aectutility, whereas is the discount factor 1/(1 +r), with r being the intertemporal rate of timepreference. An increase in past consumption raises the marginal utility of current consumptionof the addictive good and leads to an increase in current consumption. When the instantaneousutility function is quadratic and the intertemporal rate of time preference is equal to the marketinterest rate equation (1) generates a structural demand equation of alcohol consumption attime t of the following form:Ct = Ct1 +Ct+1 +1Pt +2et +3et+1(2)where Pt is the price of the addictive good at time t. Thus, current consumption ispositively related to past and future consumption and negatively related to the current price.In particular measures the eect of past consumption and, by symmetry, it also measures theeect of an increase in future consumption on current consumption of the addictive good. Thelarger the value of the greater is the degree of reinforcement or addiction.BGM recognizethat the et may be serially correlated. Even when they are not, they may aect utility ineach period and aect consumption at all dates through the optimizing behaviour implied byequation (1). Therefore BGM treat Ct1 and Ct+1 as endogenous and use lagged and forwardprices as instruments. The statistical signicance of the coecient of future consumption,together with a reasonable estimate of the intertemporal rate of time preference, provides adirect test of the rational addiction theory. Rational addicts will increase their consumptionwhen future prices are expected to fall.Equation (2) has some important implications.First, complementarity between adjacentperiods of consumption or negative cross-price elasticities between alcohol consumption atdierent points in time, holding prices in all other periods constant.Secondly, there are important dierences between long and short run responses to perma-nent price changes (a price change in the current period which is not followed by an equalchange of the opposite sign in the following periods). The short run eect describes the impacton consumption of a price change at time t and in all future time periods that is not antici-pated until time t.The long run price eect pertains to a price change in all time periods.10Since Ct1 remains the same if a price change is not anticipated until period t, the long runprice eect must exceed the short run price eect. The BGM empirical implementation ofthe RA theory has been recently criticised by Wangen (2004) who has argued that the testsof signicance rely on the assumption that the coecients under test are constant under thealternative hypothesis. By using this testing scheme we implicitly make two rather strongassumptions:a) a priori rationality of the consumer, i.e. that a price change at a point intime had been perfectly anticipated in a distant past; b) the market interest rate and theintertemporal rate of time preference are equal, so that the consumer has no inclination toshift her consumption stream either into the past or into the future with respect to her incomestream, and that both are constant over time.Tests of the RA model have been applied mainly to cigarettes consumption. Applications toalcohol consumption are not as numerous. One reason for this is that alcohol is not supposedto be as addictive as Tobacco. Another reason, put forward by Bentzen et al. (1999) isprobably the lack of long and consistent time series or panel data at the individual level.Empirical tests of RA to alcohol have been carried out by Grossman, Chaloupka and Sirtalan(1998); Waters and Sloan (1995); Baltagi and Grin (2002); Bentzen, Eriksson and Smith(1999).Grossman et al.(1998) use surveys of high school seniors as part of the monitoringof the future results to support the RA theory and they nd a long run price elasticity whichis approximately 60% larger than the short run elasticity. However, their estimates of thediscount factor are negative and implausibly high yielding negative interest rates. Waters andSloan (1995) use a cross section of individual data to test the RA model applied to alcoholconsumption. Their estimations provide relatively strong support for the RA model. Baltagiand Grin (2002), using aggregate consumption data on distilled spirits for 42 states over theperiod 1959-1994, obtain results that are in general supportive of the RA hypothesis. Howevertheir results are sensitive to the assumption of homogeneity across states or over time (Baltagiand Grin, 2002, p. 486). Finally Bentzen et al. (1999) use aggregate time series data over theperiod 1960-1994 to test the RA model for alcohol consumption across four northern europeancountries and nd support for the RA hypothesis mainly for wine and spirits whereas evidencefor beer is more controversial.114 Empirical Strategy4.1 DataWe use aggregate time series of alcoholic beverages expenditure (in millions Euro) for Italy forthe period 1960 - 2002 taken from the ISTAT National Accounts dataset. Per capita level ofexpenditure is obtained by dividing the aggregate level by the population older than 14 (calcu-lated in the middle of each year). The real price of alcohol is obtained by dividing the implicitdeator, calculated as the ratio between current expenditure on alcohol and expenditure onalcohol at 1995 prices, by the consumer price index (1995=1).Figure 2 shows the index (1995=1) of per capita expenditure on alcohol and the realprice normalised in 1995. We have also added, to the set of explanatory variables, the realexpenditure on non durables as a proxy of disposable income. Summary statistics and detailsof the data are presented in Table 3.Recently, Auld and Grootendorst (2004) have demonstrated that estimable RA modelstend to yield spurious evidence in favor of the RA hypothesis when aggregate time series areused. They do so by showing that even non addictive commodities such as milk, eggs andoranges turn out to be rationally addictive when using this kind of data. More specicallyspurious evidence for rational addiction is likely to be obtained when: 1) the consumptionseries is highly autocorrelated; 2) even a small amount of the variation in prices is endogenous;3) the value of the discount rate is exogenously imposed and, 4), overidentied instrumentalvariable estimators are used. To overcome these problems Auld and Grootendorst (2004, p.17) make some tentative recommendations for researchers that we have tried to take intoaccount.We have performed a battery of diagnostic tests on time series to avoid biases in thedirection of nding rational addiction due to the nature of the data used. First of all we havetested for price endogeneity. We perform a Hausman-Wu (HW) test6. This is a LikelihoodRatio (LR) test distributed as a 2with 34 degrees of freedom. The value of the test statisticsis 0.793 with a p-value of 1. We thus accept the null hypothesis of price exogeneity. Secondly,we have tested for stationarity of the time series using two dierent types of unit root tests:the Dickes-Fuller and Weighted Symmetric. We nd a unit root for all the variables used:P, C and Y . Therefore all estimations have been made with the variables in rst dierences.6The HW test compares the original demand equation (estimated with OLS) with the unrestricted modelthat includes,among the explanatory variables,the residuals of an auxiliary regression. In the auxiliaryregression the real price is the dependent variable whereas the explanatory variables include a linear timetrend, a constant and the aggregate quantity of Alcoholic beverages sold. See Davidson and McKinnon (1993)for a description of the Hausman - Wu exogeneity test.12Finally we have tested for autocorrelation of the dierenced variables using two dierent tests:a Durbinh alternative and a Breusch/Godfrey LM test. For all autocorretation tests we rejectthe null of no autocorrelation7and accept the hypothesis of serial correlation of order 2 in theerror terms. Results of these diagnostic tests are shown in table 4.4.2 EstimationFor our empirical implementation we write a variant of (2) with all the variables in rstdierences as follows:Ct = 0 +1Ct1 +2Ct+1 +3Pt +4Yt(3)where Yt is a proxy of disposable income and 0 is an intercept term. If the model iscorrectly specied one should have 0 = 0. However, we allow for a non-zero intercept in allestimations in order to avoid misspecication bias in the other parameters. We treat Ct1and Ct+1 as endogenous and use the Generalised Method of Moments (GMM) which producesconsistent and asymptotically ecient estimates of the parameters of interest when the errorsare serially independent. As Auld and Grootendorst (2004, p. 17) have noted, estimating themodel in dierences is likely to yield better small-sample properties than estimation in levelsfor commodities exhibiting moderate to high serial correlation in consumption. The GMMdoes estimation on a set of orthogonality conditions which are the products of equations andinstruments. These orthogonality conditions are minimized in the metric of an estimate oftheir expected covariance. Our instruments set includes three leads and three lags of boththe alcohol price and disposable income. We therefore have 10 extra instrumental variablesand test for the validity of the exceeding overidentifying conditions using the Sargan or Jtest. Standard errors are computed from an heteroscedastic-consistent covariance matrix ofthe orthogonality conditions using the White procedure. In the dierenced model we alsocontrol for serial correlation of the disturbances using a covariance matrix of the orthogonalityconditions that incorporates Moving Average (MA) disturbances of order 2.4.3 ResultsThe estimation results are shown in table 5. Looking at the dierenced model results, all theestimated parameters have the expected sign and are statistically signicant at the 95% level7The Durbins h alternative foolows a normal distribution and it is a valid test for autocorrelation whenmore than one lagged dependent variable is included in the regressors.The Breusch-Godfrey LM test for autocorrelation of order x follows a 2 distribution with DF = x +k 1,where x is the number of lags and k is the number of identied coecients in the model, including the intercept.13of signicance. The lagged consumption coecient is, consistently with the theory, largerthan the lead consumption coecient thus giving rise to a positive and reasonable rate oftime preference. The test of overidentifying restrictions is distributed as a 2with degrees offreedom equal to the number of exceeding instruments. The critical value of the 2at the 95%level of signicance with 10 degrees of freedom is 18.31, we cannot therefore reject the nullof no overidentication. Moreover, the sum of the lead and lagged consumption coecient iswell below one and amounts to about 0.71.These results are consistent with most of the recommendations of Auld and Grootendorst(2004) concerning the estimation of the RA model when using aggregate time series data. Wehave estimated the model in dierences to avoid the small sample bias that may arise whenthe studied commodity may exhibit serial correlation. Moreover the value of the intertemporalrate of time preference is not imposed ex ante as a constraint on the model.BGM have derived formulas to compute the short run (SRE) and the long run (LRE) priceelasticity of demand. Taking into account equation (2), the resulting elasticities at the samplemean, are:SRE = dCtdPtPC =21h1 2 + 1 421/2i PC(4)LRE = dCdPPC =11 (1 +) PC(5)In the rational addiction model the short run price elasticity of demand gives the percentagevariation in the consumption of an addictive good in the rst year after a permanent changein the current price and all future prices, with past consumption held constant. The long runprice elasticity gives, instead, the percentage change in consumption following a permanentprice change in all time periods (Becker, Grossman and Murphy, 1991, p. 240).The implied SRE and LRE at the sample mean of the dierenced model, are -0.26 and -0.47,respectively, and the implied intertemporal rate of time preference for the mean estimates isabout 22% (table 6). We have also calculated the implied short and long run income elasticitiesof demand. The algebraic form of the elasticities is the same as that of the price elasticities.The income elasticity of alcoholic beverages turns out to be 1.17 and 2.17. The demandfor alcoholic beverages is thus a luxury good:a 1% increase in income causes a more thanproportional increase in the demand for alcohol. This response almost doubles when thepermanent income change pertains to all time periods.According to the RA model, the marginal utility of income is a multiplying factor in thecurrent price coecient (Escario and Molina, 2001, p. 213), so that an increase in the marginal14utility of income will produce a greater increase in the price coecient. This implies that richpeople, who possess a lower marginal utility of income, will be less sensitive to price changesthan people with lower income and a higher marginal utility of income, who, instead, turn outto be more sensitive to price changes.Table 8 shows the short and long run price elasticities of demand (obtained from theparameters of the dierenced rational addiction model) estimated at the sample mean of eachdecade. The demand for alcohol appears to be rather sensitive to price changes over thesixties, when Italy experienced a lower mean level of income, than during the eighties forinstance. From 1973 to 1992 both elasticities are rather low consistently with a higher, onaverage, level of income in those years. The LRE starts to increase again from 1992 onwards,a period of austerity and restrictive economic policy in Italy. The evolution over time of theprice elasticities of demand seems thus to be consistent with the predictions of the rationaladdiction model.4.4 The Myopic Addiction ModelMyopic models of addiction fail to consider the impact of future consumption on currentconsumption, i.e. they are entirely backward looking. A myopic demand function for anaddictive good results from the solution of a static one period utility maximization problemwhere utility at time t depends on past consumption of the addictive good, as well as oncurrent consumption, income and current events. The source of dynamic behaviour is thestock of habits: St = (1 )St1 + Ct1 where is the rate at which consumption habitsdecay. Assuming a 100% rate of decay ( = 1) reduces the stock of habits to last periodconsumption and the solution to the static maximization problem produces a linear equationto be estimated of the form:Ct = +1Ct1 +2Pt +3Yt +4T +ut(6)where the subscript t denotes the time and where we have added the disposable incomeYand a time trend T. The coecient on the lagged dependent variable, in this restrictedmodel, can also be interpreted as habit persistence when the good in question is an addictivegood.The pioneering empirical work on habit persistence demand models is by Houthakkerand Taylor (1970) who estimated single equation dynamic demand functions for many goods.This model can be estimated by OLS, because it takes the lagged dependent variable as given,that is, as being uncorrelated with the equations error terms. In this case OLS estimation isconsistent (Verbeek, 2000, p. 280).15Table 7 shows the estimated coecients of the myopic model of addiction, both in levelsand in dierences, estimated as an alternative way of modeling alcohol demand. Although thelagged consumption coecient is statistically signicant in both models, the price coecientturns out to be positive, in the model in levels, and it is negative but statistically insignicant,in the dierenced model. These data, therefore, do not seem to support the myopic model ofaddiction.5 ConclusionThis study is mainly concerned with confronting the rational addiction hypothesis with Ital-ian data. To our knowledge this is the rst attempt at providing such evidence on alcoholconsumption. In the year 2000 the average per capita consumption of pure alcohol in Italywas about 7.5 litres, but according to the WHO for the European Region, the target of 6 litresper capita should be reached by the years 2015. Since the rational addiction model impliesa long run price elasticity of demand higher that alternative models of addiction, providingsuch evidence might be of help when choosing policy measures aimed at reducing alcoholconsumption.The demand for alcoholic beverages in Italy is modeled according to the rational addictiontheory by Becker and Murphy and using a long time series of data on per capita expenditure ofan aggregate of alcoholic beverages. We estimate a dierenced model, after having checked forprice exogeneity and autocorrelation of the residuals as recommended by Auld and Grooten-dorst (2004). Moreover, we do not impose a specic value of the discount rate as a constrainton the model. Although alcohol consumption is not supposed to be as addictive as Tobacco,for instance, our data t the model quite well. The future consumption coecient is positive,statistically signicant and lower than the past consumption coecient, as predicted by thetheory and we therefore get a reasonable value for the intertemporal rate of time preference.The myopic model, on the other hand, does not produce results as satisfying.We also calculate short and long run price elasticities of demand as well as the incomeelasticity of demand. Although the long run price elasticity of demand, calculated at thesample mean, is substantially higher than the short run one, as predicted by the RA model,the demand for alcoholic beverages is rather unelastic. Thus, even though alcohol consumersturn out to be actually forward looking, a price increase would not be particularly eectivein reducing alcohol demand even in the long run:a 1% permanent price change in all timeperiods would produce, according to our results, a decrease in consumption of about 0.5%.Surprisingly, the long run income elasticity of demand at the sample mean is about 2 implying16that a permanent income change of 1% in all time periods causes the demand for alcoholicbeverages to increase by more than 2%. Alcoholic beverages are therefore to be considered aluxury good the demand for which increases more than proportionally following an increase inincome. These results, however, are to be taken with caution, because the data do not allow toseparate wine, beer and spirits. Moreover, the distribution of income is not taken into accounthere. Thus, alcohol could be a luxury good at low levels of income, but a necessary or inferiorgood at high levels of income.References[1] Auld, M. C. and P. Grootendorst (2004) An Empirical Analysis of Milk Addiction. Journalof Health Economics (Forthcoming).[2] Baltagi, B. and J. Grin (2002) Rational Addiction to Alcohol:Panel data analysis ofliquor consumption. Health Economics, 11, pp. 485-491.[3] Becker, G. S. and K. Murphy (1988) A Theory of Rational Addiction. Journal of PoliticalEconomy, 96, pp. 675-701.[4] Becker, G. S., M. Grossman, K.M. Murphy (1991) Rational Addiction and the Eect ofPrice on Consumption. The American Economic Review, vol. 81, n. 2, pp. 237-241.[5] Becker, G., M. Grossman and K. Murphy (1994) An Empirical Analysis of CigaretteAddiction. American Economic Review, 84, n. 3, pp. 396-418.[6] Bentzen, J., T. Eriksson and V. Smith (1999) Rational Addiction and Alcohol Consump-tion: Evidence from the Nordic Countries. Journal of Consumer Policy, 22, pp. 257-279.[7] Cook, P. J. and M.J. Moore (2000) Alcohol. In: Handbook of Health Economics, Vol.1B, edited by A.J. Culyer and J.P. Newhouse. Elsevier Science, 2000.[8] Davidson, McKinnon (1993). Estimation and Inference in Econometrics. Oxford Univer-sity Press, Oxford.[9] Escario, J. J., J. A. Molina (2001) Testing for the Rational Addiction Hypothesis inSpanish Tobacco Consumption. Applied Economics Letters, 2001, n. 8, pp. 211-215.[10] Grossman, M., F. Chaloupka and I. Sirtalan (1998) An empirical analysis of alcoholaddiction: results from Monitoring the Future Panels. Economic Inquiry, 36, pp. 39-48.17[11] Houthakker, H. S., L. D. Taylor (1970) Consumer Demand in the United States, 1929-1970: Analysis and Projections, 2nd Edn. (Harvard University Press, Cambridge, MA).[12] Klingemann, H. (2000) Alcohol and its social consequences - the forgotten dimension.Copenhagen, WHO Regional Oce for Europe, 2000 (keynote paper for the WHO Eu-ropean Ministerial Conference on Young People and Alcohol, Stockholm, 19-21 February2001).[13] Ministero della Salute (2003) Relazione del Ministro della Salute al Parlamento sugliInterventi Realizzati ai Sensi della Legge 30.3.2001 n. 125 Legge Quadro in Materia diAlcol e Problemi Alcolcorrelati.[14] Rehn, N., R. Room and G. Edwards (2001) Alcohol in the European Region - consump-tion, harm and policies. WHO Regional Oce for Europe, 2001.[15] Scafato, E., S. Ghirini and R. Russo (2004) I consumi alcolici in Italia. Report 2004 suiconsumi e le tendenze (1998-2001). MIMEO, Istituto Superiore di Sanit; OsservatorioFumo, Alcol e Droga.[16] Scafato, E., R. Russo (2004) La donna e lalcol. Tendenze nei consumi e strategie diintervento. Annali dellIstituto Superiore di Sanit, vol. 40, n. 1, 2004.[17] Verbeek, M. (2000) A Guide to Modern Econometrics. John Wiley and Sons Ltd, WestSussex, England.[18] Wangen, K. R. (2004) Some Fundamental Problems in Becker, Grossman and MurphysImplementation of Rational Addiction Theory. Discussion Papers n. 375, April 2004;Statistics Norway, Research Department.[19] Waters, M. and F. A. Sloan (1995) Why do people drink ? Applied Economics, 27, pp.727-736.18Table 1: Share of Alcohol Consumers in Italy1998 1999 2000 2001 % Var. 98-01Male 83.4 83.1 87.2 87.7 5.2Female 58.6 59.1 63.6 63.1 7.7Source: Scafato, Ghirini and Russo, 2004, p. 2Table 2: Average per capita Consumption of AlcoholicBeverages and Pure Alcohol in Italy (Litres/Year)1981 1991 2000 % Var. 81-00Wine 86.2 62.1 51 -40.8Beer 17.9 24.9 28.1 +57Spirits 3.5 2.5 1.2 -65.7Pure Alcohol 11.7 9.1 7.5 -35.9Source: Scafato and Russo, 2004, p. 419Table 3: Denitions, Means (M) and Standard Deviations (SD) of VariablesVariable Denition, Mean (M), Standard Deviation (SD)Ct = (ALQt/N14)100 Per capita (of persons aged >14 ) alcohol expenditureper year (1995=1); (M=1.351; SD=0.241)Pt = (ALVt/ALQt)/PGtReal price of alcoholic beverages (1995=1)(M=1.139; SD=0.161)Yt = Y 95t/N14 Real per capita disposable income per year (1995=1)(M=0.783; SD=0.234)ALQtAggregate expenditure in alcoholic beverages, at constant1995 prices (millions Euro); (M=5417.678, SD=837.055)ALVtAggregate expenditure in alcoholic beveragesat current prices (millions Euro); (M=2577.546; SD=1882.417)PGtConsumer Price Index (CPI) (1995=1)(M=0.481; SD=0.406)Y 95tHouseholds nal consumption expenditure per year at constant1995 prices (millions Euro); (M=391066.923; SD=145025.636)N14 Population older than 14 (in millions units)calculated in the middle of each year; (M=44.050; SD=3.751)20Table 4: Diagnostic Tests on Time Series (p-values in parentheses)1. Price 2. Unit Root 3. Autocorrelationexogeneity P C Y (dierenced model)1. LR(HW) test 0.793(1.000)2a Wtd. Sym. -1.515 -0.798 -1.166(0.887) (0.985) (0.957)2b Dickey-Fuller -1.233 -2.251 0.567(0.903) (0.461) (0.997)3a Durbins h Alt. -2.327(0.020)3b Breusch-Godfrey 5.767(0.056)21Table 5: GMM Estimates of the Rational Addiction ModelDependent variable Ct (standard errors in parentheses)DifferencesCt10.392(0.083)*Ct+10.322(0.113)*Pt-0.161(0.089)*Yt1.073(0.336)*Constant -0.025(0.007)*Test of overident. restrict. 8.379p-value 0.592R2(adj.) 0.314n 37*= standard errors are both heteroscedastic consistent and are also robust to autocorrelation:the disturbance terms are specied as a second order moving average process.Table 6: Short and Long Run Elasticities of demandat the sample mean (standard errors in parentheses)SRE LRE r(%)Price elasticity -0.256 -0.474 21.952(0.190) (0.385)Income elasticity 1.173 2.173(0.205) (0.596)Standard errors have been computed using the Delta method.Elasticities have been computed from the parameters of the RA dierenced model;22Table 7: OLS Estimates of the MyopicModel (standard errors in parentheses)Levels DifferencesCt10.818 0.337(0.047) (0.012)Pt0.104 -0.032(0.063) (0.207)Yt1.178 1.649(0.334) (0.423)T -0.025 -(0.006)Constant -0.257 -0.035(0.144) (0.012)R2(adj.) 0.976 0.344n 42 41Standard Errors are heteroscedastic consistent;Table 8: Short and Long Elasticities of demandover sub-periods (standard errors in parentheses)*SRE LRE1963 1972 -0.27 -0.52(0.207) (0.419)1973 1982 -0.22 -0.42(0.165) (0.334)1983 1992 -0.21 -0.38(0.154) (0.311)1993 1999 -0.31 -0.58(0.232) (0.470)* Standard errors for elasticities have been computed using ANALYZ in TSP45.Elasticities have been computed from the parameters of the RA dierenced model23