Settling the Smoke: Public Policy and Shareholder Wealth in the Cigarette Industry

SETTLING THE SMOKE: PUBLIC POLICY AND SHAREHOLDERWEALTH IN THE CIGARETTE INDUSTRY

ROSSITZA B. WOOSTER and CRAIG A. GALLET

This article uses daily data on stock returns of five U.S. publicly traded cigaretteproducers to document the wealth effects of antismoking policies initiated in theperiod 1964 to 1971. The authors find significant abnormal returns across 23 datescorresponding to important regulatory events. The second-stage estimation showsthat wealth effects are also influenced by firm characteristics, such as market share,advertising intensity, percentage of sales of filter-tip cigarettes, percentage of ad-vertising expenditures devoted to TV and radio, and tobacco leaf inventories. Overall,the article estimates that industry losses from antismoking policies amounted toapproximately $1.5 billion. (JEL L66, G14, I18)

I. INTRODUCTION

Although tobacco smoking was touted inearlier years as an aid for a variety of ailments,in light of the medical evidence on the detri-mental health effects of smoking, the federalgovernment took steps throughout the 1960sto raise consumer awareness of the dangersof smoking. For example, the 1964 Reportof the Surgeon General was one of the earliestpublic acknowledgments that smoking in-creases the risk of developing lung cancer.This was followed by the passage of the Fed-eral Cigarette Labeling and Advertising Actin 1965, which required warning labels to beplaced on cigarette packages. Then in 1967the Federal Communications Commission(FCC) applied the Fairness Doctrine to ciga-rettes by mandating that broadcasters provideairtime for antismoking messages. However,application of the Fairness Doctrine soonended after Congress passed the 1970 PublicHealth Cigarette Smoking Act, which bannedcigarette advertising on TV and radio from1971 onward.1 In subsequent years, new warn-

ings from the surgeon general, lawsuits leviedagainst the industry, and recommendations toregulate tobacco as a drug all contributed tofurther declines in cigarette demand.

Although antismoking policies are ex-pected to reduce the demand for cigarettes,several studies posit a relationship betweensuch policies and the supply side of the mar-ket. For instance, Barnett et al. (1995) findthat competition increased in the cigaretteindustry following the release of the 1964surgeon general’s report, whereas Eckard(1991) finds a reduction in competition afterthe 1971 advertising ban. Also, Farr et al.(2001) and Gallet (2003) find the FairnessDoctrine and the advertising ban intensifiedthe effect of advertising on market power.

Nonetheless, an unfortunate limitation ofthese studies is that arguments for antismokingpolicies affecting cigarette supply are based onmodels offirmbehavior, yet industry-level datais typically used in the estimation of the em-pirical model.2 As an alternative, event studymethodology relies on firm-level security pricedata to investigate the effects of antismokingpolicy ‘‘events’’ on cigarette producers, withthe argument being that policies enhancingWooster: Assistant Professor, Department of Economics,

6000 J Street, California State University, Sacramento,CA 95819. Phone 1-916-278-7078, Fax 1-916-278-5768, Email [email protected]

Gallet: Assistant Professor, Department of Economics,6000 J Street, California State University, Sacramento,CA 95819. Phone 1-916-278-6099, Fax 1-916-278-5768, Email [email protected]

1. Several studies (e.g., Baltagi and Levin, 1986; Ham-ilton, 1972; Schneider et al., 1981) find reductions in cig-arette demand resulting from antismoking policies of the1960s. See Chaloupka and Warner (2000) and Gallet andList (2003) for surveys of this literature.

ABBREVIATIONS

CAR: Cumulative Abnormal ReturnFCC: Federal Communications CommissionFTC: Federal Trade Commission

2. An exception is Eckard (1991), who uses firm-leveldata to analyze market share instability before and afterthe 1971 advertising ban.

211Contemporary Economic Policy doi:10.1093/cep/byi016(ISSN 1074-3529) � Western Economic Association International 2005Vol. 23, No. 2, April 2005, 211–223 No Claim to Original U.S. Government Works

(diminishing) profit expectationswill have pos-itive (negative) effects on shareholder wealth.However, because existing event studies ofthe cigarette industry either focus on differentantismoking policies or define regulatory eventdates in different ways, it is difficult to reacha consensus regarding the relative importanceof policies. For instance, Scheraga andCalfee’s(1996) studyof theearly1960sfinds little impactof the Surgeon General’s Report on the stockreturns of impacted firms; studies that focuson the late 1960s (e.g., Johnson et al., 1991;Lamdin, 1999; Mitchell and Mulherin, 1988)find abnormal returns associated with the1971 advertising ban. To the best of theseauthors’ knowledge, though, a study encom-passing all relevant policies throughout theentire decade has not been done.

Comparisons across existing event studiesare further stymied by differences in empiricalprocedures. For example, Mitchell andMulherin (1988) test for effects on shareholderwealth by regressing a portfolio of cigarettecompany returns on a market portfolio ofreturns and a dummy variable (set equal toone for every month between July 1968 andDecember 1970). The authors interpret the es-timated positive coefficient of the dummy vari-ableasanindicatorof thepositivewealtheffectsassociatedwith the 1971 advertising ban.How-ever, this interpretation may be inaccurate be-causetheauthorsaggregatethewealtheffectsofmultiple regulatoryevents intoa single cumula-tive abnormal return measure. Indeed, whenthe event window is reduced to less than twoandahalfyears,Lamdin(1999) fails tofindpos-itive abnormal returnswithmonthlydata in thecigarette industry. Hence, to control for suchambiguity, a consistent procedure should beused across all events.

In light of these limitations, the purpose ofthis article is to reexamine the effect of anti-smoking policies on shareholder wealth byexpanding on the current literature in severalways. First, to minimize the influence of eventsnot related to antismoking policies on the es-timated wealth effects, this article uses dailystock price data for the five U.S. publiclytraded cigarette producers (i.e., AmericanBrands, Liggett, Lorillard, Philip Morris,and R.J. Reynolds) between 1964 and 1971.3

Second, the authors identify a comprehensiveset of 23 important dates pertaining to the useof information as a deterrent to smokingor legal measures to regulate the industry’sadvertising practices. Obtaining significantabnormal returns across these dates, the anal-ysis then draws comparisons between theeffects of different antismoking initiatives onshareholder wealth. Finally, given thatScheraga and Calfee (1996) find differencesin abnormal returns across firms, the authorspay attention to firm-level nuances. In partic-ular, the authors use a variety of firm char-acteristics (i.e., market share, advertisingintensity, percentage of sales associated withfilter-tip cigarettes, percentage of advertisingexpenditures devoted to TV and radio, and to-bacco leaf inventories) to further analyze dif-ferences in the wealth effects across firms.

In the remainder of the article, section IIoffers several arguments for how the profit ofcigarette producers may have been affectedbyantismoking policies.This is followed in sec-tion IIIwith adiscussionof the event dates cho-sen for the study. The estimationmethodologyis provided in section IV, followed by the esti-mation results in section V. The article con-cludes with a summary in section VI.

II. THEORY CONSIDERATIONS

The literature provides several argumentsfor how antismoking policies of the 1960smay have affected firm profits and thereforeshareholder wealth. Each of these is discussedseparately in this section.

A. Demand Reductions andOligopoly Coordination

Models of oligopoly behavior over the busi-ness cycle provide insights into the potentialeffects of antismoking policies on firm profits.In particular, with the intended effect of anti-smoking policies being to reduce cigarettedemand, a rational forecast in earlier yearswould be to expect lower future demand andprofit. As a consequence, following severalgame-theoretic studies (i.e., Bagwell andStaiger, 1997; Haltiwanger and Harrington,1991; Rotemberg and Saloner, 1986), if firmsexpect future profit to be lower this creates thetemptation to cheat onany cooperative solutionin the industry, resulting in more competitive

3. Except for Johnson et al. (1991), previous studies ofwealth effects associated with antismoking policies haveused monthly stock price data to identify abnormalreturns.

212 CONTEMPORARY ECONOMIC POLICY

pricing and lower profit.4 In the end, therefore,policies forwhich thepredominanteffect is tore-ducethefuturesizeofthemarketshouldcoincidewith negative stock price reactions.

B. Advertising and Competition

With several studies finding an impact ofadvertising on competition in the cigarette in-dustry, another avenue exists for advertising-re-lated policies to affect shareholder wealth. Yetthe evidence ismixed in the literature.Forexam-ple,studiesthatestimatesupplyrelationshipsforthecigarette industry (e.g.,Farretal., 2001;Gal-let, 2003; Tremblay and Tremblay, 1995) findthat increases in advertising lead to increasedmarket power in the industry. Such results sup-port the anticompetitive view of advertising,whichpredicts that if incumbentfirmsuseadver-tising to deter entry (by forcing entrants to ag-gressively advertise to secure market share),then policies that reduce advertising will stimu-late competitionand thereforeweakenprofit. Insuch cases, the industry would not welcomeadvertising restrictions, because they woulddampen shareholder wealth.

In contrast, the procompetitive view of ad-vertising maintains that better information toconsumers regarding price, quality, and so on,increases consumer awareness of the availabil-ity of substitutes, and thus policies that reduceadvertising block an effective means of enter-ing the market. Such policies would thereforebe welcome by the industry, because theywould weaken competition, enhance the profitof incumbents, and therefore boost share-holder wealth. The procompetitive view of ad-vertising finds support in studies by Mitchelland Mulherin (1988) and Eckard (1991)who focus on the 1971 advertising ban. In par-ticular, Mitchell and Mulherin’s (1988) eventstudy finds that market reactions to regulatoryevents leading up to the adoption of the adver-

tising ban are associated with value creation.This suggests that financial markets viewedthe advertising ban in a positive light (i.e., pro-fits were expected to increase after the ban),which would be consistent with a reductionin competition following the immediate dropin advertising resulting from the ban.5 Eckard(1991), on the other hand, finds that theban coincided with greater stability of marketshares, less brand entry, and higher price-costmargins, which are all indicative of a reductionin competition and therefore also support theprocompetitive view of advertising.6

In light of the opposing evidence on the roleof advertising in the cigarette industry, it re-mains unclear whether events leading up to ad-vertising-related policies had positive ornegative effects on shareholder wealth. Theempirical results presented in section V shedfurther light on this issue.

C. Objectives of Regulation

Depending on the objective function of reg-ulators, antismoking policies may have beenviewed positively or negatively by the industry.For instance, if regulators were driven solelyby a concern for public health, then policiesdesignedtoreducetobaccoconsumptionwouldpresumably have an ill effect on profits andshareholder wealth in the industry. Indeed, inthe case of the Surgeon General’s Report, it ishard to find evidence that firms benefited fromthe health warning.7

4. The game-theoretic literature addresses strategiesthat can be used to sustain cooperation among membersof a cartel. In particular, within the framework of a re-peated game, cheating on a collusive agreement today isdeterred by the threat of punishment tomorrow. Hence,if profit is expected to be lower tomorrow, the cost ofcheating today (in terms of the sacrifice of future collusiveprofit during the punishment phase) is lower. With the rel-ative benefit from cheating increasing, this stimulates firmsto cheat today (i.e., undercut the cooperative price or pro-duce beyond the quota level of output). Therefore, tomitigate the temptation to cheat, the cartel must behavemore competitively today by reducing price or increasingcooperative output.

5. However, with modifications to the empirical proce-dure,Johnsonetal. (1991)andLamdin(1999)fail tofindsim-ilar stock price reactions surrounding the advertising ban.

6. Interestingly, according to Adams and Brock(1999), even policies not specifically directed toward themarketing of cigarettes may have affected the role of ad-vertising in the industry. In particular, prior to the volumeof health information, producers often directed advertis-ing toward the relative safety of their product. Hence, itwas not uncommon for a cigarette producer to claim itsbrand was healthier than rival brands. As the severityof the health effects of smoking became more publicized,however, agreements were reached in the industry to redi-rect advertising away from claims about the health bene-fits of one brand over another.

7. In fact, for years the industry sought to discredit anyreferences to smoking and health. For example, in aninternal memo dated January 29, 1964 (taken from http://tobaccodocuments.org) written by the president of PhilipMorris (George Weissman), in response to the release ofthe surgeon general’s report, at one point Mr. Weismanstates ‘‘We must in the near future provide some answerswhich will give smokers a psychological crutch and a self-rationale to continue smoking. These answers must alsopoint up the weaknesses in the Report and the path forfuture research.’’

WOOSTER & GALLET: WEALTH EFFECTS FROM ANTISMOKING POLICIES 213

http://tobaccodocuments.org

http://tobaccodocuments.org

Yet Stigler (1971), Peltzman (1976), andothers offer an alternative motivation behindregulation that may be applicable to adver-tising-related policies. Namely, if regulatorsbecome ‘‘captured’’ by the industry (i.e., oper-ate on behalf of the industry), provided adver-tising is procompetitive, the industry wouldfind it in its best interest to lobby for restric-tions on advertising. By doing so, competitionwould be reduced and profits enhanced. In-deed, this is suggested by Mitchell andMulherin’s (1988) findings of a positive stockreaction to the advertising ban. Moreover,Gormley (1979) finds that members of theFCC, who are responsible for administeringthe Fairness Doctrine, often voted in favorof the broadcast industry, particularly if theywere formerly employed in that industry.Such proindustry behavior may have had apositive effect on the cigarette industry, per-haps by postponing the application of theFairness Doctrine to cigarette advertising orenforcing a weaker version of the doctrine.

III EVENT DATES

When conducting an event study, key dates inthe adoption of a policy change must first beidentified. Because this study focuses on anti-smoking policies initiated in the period be-tween the release of the 1964 SurgeonGeneral’s Report and the 1971 advertisingban, the authors follow the approach takenby existing studies. For example, with respectto the legislationof 1965 (i.e., cigarettewarninglabels) and1970 (i.e., advertisingban), this articlefollowsMitchell andMulherin (1988) and Lam-din (1999) by defining event dates according tothe process bywhich each bill became law. Eventdates were then determined by a search throughthe major publications of the time. The discus-sion to follow presents the events and their cor-responding dates in chronological order.

The first event was the release of the 1964Surgeon General’s Report on January 12,1964. Soon after, on April 28, 1964, producersin the cigarette industry adopted aCode ofAd-vertising to self-regulate the industry, suchthat producers whose advertisements were di-rected toward youths ormade unproven healthclaims regarding a particular brand could befined up to $100,000.

A series of eight events ultimately led torequiring that health warnings be placed on

cigarette packages. To begin, on June 25,1964, the Federal Trade Commission (FTC)voted in favor of requiring warning labels oncigarette packages. However, it postponedmoving on this pending legislation that wasbeing proposed in Congress. Indeed, the SenateCommerce Committee voted in favor of warn-ing labels legislation on April 28, 1965. Thiswas followed by a similar vote of the HouseCommerce Committee on May 28, 1965. Thefull Senate approved its version of the bill onJune 17, 1965; the full House approved its ver-sion on June 23, 1965. The final bill wasapproved in conference on July 2, 1965, andsigned into law on July 28, 1965.Warning labelsthen became law effective on January 2, 1966.8

The next 13 events dealt directly with theissue of advertising. For instance, on June 3,1967, the FCC ordered that the Fairness Doc-trine be applied to cigarettes. That is, althoughnot required to match on an equal time basis,the FCC required all TV and radio stationsto broadcast antismoking messages. Then,following the dates provided by Mitchell andMulherin (1988) and Lamdin (1999), theFTC and FCC voted on July 1, 1968, and Feb-ruary 6, 1969, respectively, to recommend thatCongress ban cigarette advertisements fromTV and radio. Subsequently, the House Com-merce Committee voted on May 29, 1969, infavor of a bill to ban cigarette advertising,whichwas then voted in favor by the fullHouseon June 19, 1969.9 The Senate CommerceCommittee voted in favor of a similar bill onNovember 6, 1969, which the full Senateapproved on December 13, 1969. A House-Senate conference reached an agreement onthe bill on March 4, 1970; it was signed intolaw on April 2, 1970. Then on November 13,1970, the industry ended its Code of Adver-tising, and with prosmoking advertisementssoon to end on TV and radio, the FCC an-nounced an end to the application of the Fair-ness Doctrine to cigarettes on December 16,1970. Finally, the advertising ban became ef-fective on January 2, 1971, one day after theheavily advertised college football bowl games.

8. Interestingly, both the House and Senate versionsof the bill were relatively weak, because they refused torequire that health warnings also be attached to advertise-ments. They also barred the FTC from regulating cigaretteadvertising for several years.

9. Also important, related to the FCC’s ability toregulate the cigarette industry, on June 10, 1969, theU.S. Supreme Court upheld the constitutionality of theFairness Doctrine.


Table 1 provides a summary of the 23events, as well as the expected effects onshareholder wealth (indicated as positive þ,negative �, or ambiguous �/þ), dependingon the most likely applicable theory from sec-tions II. A few points are worth noting. First,because most events are expected to lead toan eventual reduction in cigarette demand(except for the elimination of the FairnessDoctrine on December 16, 1970), the discus-sion of stock price reactions to demand reduc-tions (see section IIA) is applicable to many ofthe events. Events dealing predominantly withadvertising (such as the advertising ban) orregulations (such as warning labels, the Fair-ness Doctrine, and the advertising ban) are lesscommon. Second, events that focus on inform-

ing consumers of the health consequences ofsmoking (such as the release of the surgeongeneral’s report or the initial proposals for re-quiring warning labels) are likely driven byregulators’ desires to reduce cigarette demand(see sections IIA and IIC), and hence theauthors expect a negative reaction of share-holder wealth to such events.10 Third, forother events the expected reaction of share-holder wealth is ambiguous. This is due to

TABLE 1

Antismoking Policies Initiated between 1964 and 1971: Dates and Expected Signs

Expected Sign of CARs

Calendar Date Event IIA IIB IIC

Jan. 12, 1964 1964 Report of the Surgeon General �Apr. 28, 1964 Code of Advertising adopted � �/þJun. 25, 1964 FTC proposes health warnings be placed on cigarette packages � �Apr. 28, 1965 Senate Commerce Committee approves warning labels bill � �May 28, 1965 House Commerce Committee approves warning labels bill � �Jun. 17, 1965 Senate approves bill (FTC prohibited from regulating

advertising for 3 years)� �/þ

Jun. 23, 1965 House approves bill (FTC prohibited from regulatingadvertising forever)

� �/þ

Jul. 2, 1965 House-Senate conference approves common bill (FTC prohibitedfrom regulating advertising for 4 years)

� �/þ

Jul. 28, 1965 Federal Cigarette Labeling and Advertising Act signed into law � �/þJan. 2, 1966 Warning labels become effective � �/þJun. 3, 1967 FCC orders the Fairness Doctrine be applied to cigarette advertising � �/þJul. 1, 1968 FTC votes in favor of recommending to Congress that all cigarette

advertisements be banned from TV and radio� �/þ �/þ

Feb. 6, 1969 FCC offers similar proposal � �/þ �/þMay 29, 1969 House Commerce Committee approves bill to ban advertising on

TV and radio� �/þ �/þ

Jun. 10, 1969 Supreme Court upholds the constitutionality of the FairnessDoctrine

� �/þ

Jun. 19, 1969 House approves bill � �/þ �/þNov. 6, 1969 Senate Commerce Committee approves bill to ban advertising

on TV and radio� �/þ �/þ

Dec. 13, 1969 Senate approves bill � �/þ �/þMar. 4, 1970 House-Senate conference approves common bill � �/þ �/þApr. 2, 1970 Public Health Cigarette Smoking Act signed into law � �/þ �/þNov. 13, 1970 Code of Advertising ended � �/þ �/þDec. 16, 1970 Fairness Doctrine ended þ �/þJan. 2, 1971 Advertising ban becomes effective � �/þ �/þ

Notes:Expected impact on shareholder wealth captured by CARs. Expected signs of CARs are provided, depending onthe most applicable theories from section II. Section IIA coincides with demand reduction and oligopoly coordinationtheories, IIB coincides with advertising and competition theories, and IIC coincides with objectives of regulation theories.

10. Similar to the Surgeon General’s Report, theindustry’s position was against the placement of healthwarnings on cigarette packages. For example, in Weiss-man’s (1964) memo, he suggested that the industry fightany labeling action. Therefore, early on one can expectevent dates corresponding to warning labels to be viewednegatively.


the different arguments of the competing the-ories. For example, because several events ad-dress advertising issues, following section IIB,if advertising is procompetitive (anticompeti-tive), then events that seek to lower advertisingwill have positive (negative) stock price re-actions. Moreover, per the discussion in sec-tion IIC, if regulators are driven mainly byconcerns for consumer (producer) well-being,then regulations will have negative (positive)stock price reactions. For example, associatedwith the events of June 17, 1965, through Jan-uary 2, 1966, it may be that the weaker versionof warning labels that emerged is an indicatorof the industry’s ability to effectively lobbyCongress for less intrusive warning labels.Also, depending on the lobby abilities of theindustry, because the application of the Fair-ness Doctrine may have been less severe thanexpected, it is difficult to sign the expectedreaction of returns to events surroundingthe Fairness Doctrine, with regard to theobjectives of regulators.

Because of the variety of possible reactionsof shareholderwealth to theseevents, it remainsof interest to examine the change in shareholderwealth throughout thisperiod.Thenext sectionoutlines the estimation methodology.

IV ESTIMATION METHODOLOGY

The application of event study methodol-ogy in welfare analysis of regulatory changesis a common approach to evaluating the firm-level market impact of regulatory changes ineconomics and finance (see, for example,Binder, 1985), Lamdin, 2001; MacKinlay,1997). Assuming markets are efficient, infor-

mation contained in the public announcementof a regulatory policy will be capitalized in thesecurity prices of firms affected by that policy.If investors perceive the regulation to result ina higher stream of current and future profits,then the security prices of the impacted firmswill rise and vice versa. The abnormal returnon a firm’s security is thus a positive or nega-tive change in the normal return measured af-ter controlling for general market volatility.

The discussion in section II offers severalscenarios for how antismoking policies canaffect the profits of cigarette companies, im-plying variation in abnormal returns acrossevents. In addition, differences in firm char-acteristics, such as company size, inventories,and advertising intensity, make it unlikely thatthe abnormal returns associated with a givenevent will be uniform across companies. Al-though evidence on the impact of a given pol-icy on shareholder wealth can, on average, besummarized by significance, signs, and magni-tude of the abnormal returns for that event,variation in abnormal returns across eventsand firms can be further exploited to examinewhat factors contribute to gains or losses inshareholder wealth from antismoking policies.

Thus the estimation methodology pre-sented next is in two stages. In the first stage,the authors employ event study methodologyto estimate abnormal stock returns for eachof the five publicly traded cigarette companiesover the 23 events outlined in section III. Inthe second-stage analysis, the authors usethe estimated abnormal returns to further in-vestigate how firm-specific factors determinethe observed market effects associated withthe 23 events.

TABLE 2

Predicted Impacts of Second-Stage Explanatory Variables on Absolute Value of CARs

Variable Prediction

Market share (MS) Negative (if bigger firms are insulated from anti-smoking policies);Positive (if bigger firms are bigger targets of anti-smoking policies)

Advertising-to-sales ratio (ASR) Negative (if firms with greater advertising have greater brandloyalty); positive (if firms with greater advertising are moresusceptible to anti-advertising events)

Percent filter cigarettes (PERFILT) Negative (if filter cigarettes are viewed as being less harmful)

Percent TV and radio advertisements (PERTV) Positive (if higher broadcast advertising coincides with firms beingmore affected by advertising policies geared toward TV and radio)

Tobacco leaf inventory-to-sales ratio (ISR) Positive (if firms with higher inventories are more susceptible toanti-smoking policies which reduce demand)

Note: CARs for each firm measure the change in shareholder wealth from antismoking regulatory events initiatedbetween 1964 and 1971.


A. First-Stage Estimation

Estimation of abnormal returns in the firststage begins with the identification of the dateson which the 23 events discussed in section IIIwere publicly announced. Because the eventsof interest occur on the same date for allfive firms (i.e., there is total clustering), theauthors adopt the methodology outlined inMacKinlay (1997) and obtain the abnormalreturns by estimating a multivariate regressionmodel with unaggregated security data anddummy variables for each event:11

Rit ¼ ai þ biRmt þXj¼23

j¼1

dijDjt þ eit:ð1Þ

In equation (1), Rit denotes the return on thesecurity of firm i on day t, Rmt is the marketreturn on day t as measured by the value-weighted index compiled by the Center forResearch in Security Prices, Djt is a dummyvariable set equal to one for event date jand zero otherwise, and eit is a zero mean dis-turbance term. Estimates of dij in equation (1)produce the desired measure of the abnormalreturn for firm i corresponding to event j.

To capture as much of the event’s impact aspossible, it is standard to include in the esti-mation of abnormal returns more than onetrading day’s worth of security returns infor-mation. Specifically, to better capture whenthe market learned of new information relatedto the passage of antismoking policies, the

TABLE 3

Three-Day and Thirty-One-Day Cumulative Abnormal Returns from Antismoking Policiesbetween 1964 and 1971

American Brands(N ¼ 2,331)

Liggett(N ¼ 2,330)

Lorillard(N ¼ 1,509)

Philip Morris(N ¼ 2,331)

R.J. Reynolds(N ¼ 2,331)

Event Date 3-Day 31-Day 3-Day 31-Day 3-Day 31-Day 3-Day 31-Day 3-Day 31-Day

Jan. 12, 1964 �0.22a �0.12 0.80 �0.09 �0.95a �0.26 �0.63a �0.29 �0.15 �0.22

Apr. 28, 1964 �0.69b �0.16 �0.76a �0.10 0.33 �0.33b �0.16b �0.01 �2.02a �0.08

Jun. 25, 1964 �1.88a �0.03 �0.93a 0.08 �0.95a �0.03 �1.16a �0.33a �1.46a �0.14

Apr. 28, 1965 �1.23a 0.13 �0.30a 0.11 �0.76c �0.03 �0.20c 0.18c �0.64a 0.10

May 28, 1965 �0.21a �0.19 �0.40a �0.22b �0.20 �0.05 �0.91a �0.17 0.13b �0.14

Jun. 17, 1965 0.06 0.16 �0.17a 0.09 �0.46a 0.03 �0.39b �0.13 �0.30a 0.10

Jun. 23, 1965 0.06 0.08 0.05 0.54a 0.82a �0.07 0.03 0.27 0.11 0.17

Jul. 2, 1965 0.28b �0.18 0.64a �0.45a 0.01 0.28b �0.60a �0.31 0.31c �0.29c

Jul. 28, 1965 0.24 0.21c 0.15 �0.1E-5 0.12 �0.10 0.52 0.25 0.19b 0.25

Jan. 2, 1966 0.43a �0.02 0.38b 0.05 0.50b 0.06 �0.27c �0.03 0.65a �0.02

Jun. 3, 1967 �0.43c �0.07 0.10 0.04 0.01c 0.07 1.097a 0.32 0.24 �0.04

Jul. 1, 1968 �1.09a 0.18 �0.82b 0.27c 0.65c 0.67a �1.59a �0.02 �1.36a 0.11

Feb. 6, 1969 �1.35b 0.13 �0.47 �0.22 — — �1.56a �0.21 �0.59 �0.17

May 29, 1969 �1.59a 0.29 �0.28a 0.05 — — �0.05 0.69a �0.01 0.25b

Jun. 10, 1969 �0.52a �0.27 �0.59a �0.05 — — �0.79a �0.10 �0.07 0.01

Jun. 19, 1969 �0.32a �0.08 0.14 �0.13 — — 0.14 �0.20 0.42b �0.08

Nov. 6, 1969 0.41a 0.35a 0.11 �0.07 — — �0.10b 0.42 0.19 0.16

Dec. 13, 1969 �0.06 �0.25c �0.63a �0.7E-5 — — 0.06c 0.26 0.22c �0.11

Mar. 4, 1970 �0.12 0.08 0.76a 0.58a — — 0.06 0.11 �1.50a 0.10

Apr. 2, 1970 �0.07 0.26 0.35a �0.09 — — �1.96a �0.09 �0.36a 0.23

Nov. 13, 1970 �0.27c 0.38b �0.31 0.41a — — 0.46b 0.21b 0.35 0.29

Dec. 16, 1970 �0.01 �0.30 0.46 �0.27c — — 0.18 �0.46b 0.06 �0.03

Jan. 2, 1971 �0.06 0.03 0.22b 0.09 — — �0.27a 0.24 �1.58a 0.10

Note: a, b, and c denote significance at the 1%, 5%, and 10% levels, respectively. Results coincide with estimates of dij inequation (1).

11. Another method to accommodate cross-correla-tion of abnormal returns is to aggregate the abnormalreturns into a portfolio. As discussed in MacKinlay(1997), the advantage of the multivariate approach overthe portfolio approach is that the former allows for thepossibility that some firms have positive abnormal returnsand some firms have negative abnormal returns for a givenevent. This information is vital for the second-stage regres-sion, in which the authors explore the variation in abnor-mal returns in more detail.


authors estimate abnormal returns usinga three-day window including the day before,the day of, and the day after the event (i.e.,Djt–1 ¼Djt ¼Djtþ1 ¼ 1 and 0 otherwise). Witha three-day event window the interpretation ofd̂ij is that of a cumulative abnormal return(CARij). Because this window most closelycoincides with events immediately related tothe dates discussed in the preceding section,it is the preferred window to identify abnormalreturns.

Using a three-day window is an approachsimilar to Johnson et al. (1991), but otherssuch as Mitchell and Mulherin (1988) andLamdin (1999) have used average monthlyreturns to estimate abnormal returns froma regulatory event announced within thatmonth. Yet extending the event window be-yond three trading days, although not unusualin event studies, is costly as the probability ofcapturing stock price reactions to unrelatedevents increases with the size of the window.Nevertheless, the authors also estimate equa-

tion (1) with a one-month event window asa point of comparison to those studies thathave used monthly data to estimate abnormalreturns.

B. Second-Stage Estimation

In the second-stage analysis, the authorsuse the estimated abnormal returns from thefirst stage to explain the firm-level marketeffects associated with antismoking policies.12

In particular, the authors pool the esti-mated abnormal returns from the three-dayevent window across the five firms and usethem as the dependent variable in a panelmodel to evaluate whether firm-specific

TABLE 4

Wealth Effects from Regulatory Changes in the Cigarette Industry: 1964–71 (Millions of Dollars)

Event date American Brands Liggett Lorillard Philip Morris R.J. Reynolds Industry

Jan. 12, 1964 �1.64 2.35 �2.83 �1.69 �2.61 �6.41

Apr. 28, 1964 �6.19 �2.42 1.02 �0.47 �40.24 �48.30

Jun. 25, 1964 �16.31 �2.82 �2.72 �3.29 �26.84 �51.99

Apr. 28, 1965 �12.43 �0.99 �2.29 �0.65 �11.12 �27.47

May 28, 1965 �2.06 �1.29 �0.59 �2.93 2.18 �4.69

Jun. 17, 1965 0.56 �0.55 �1.28 �1.17 �4.93 �7.37

Jun. 23, 1965 0.57 0.16 2.27 0.09 1.82 4.90

Jul. 2, 1965 2.62 2.07 0.03 �1.79 5.04 7.96

Jul. 28, 1965 2.29 0.48 0.35 1.53 3.12 7.77

Jan. 2, 1966 4.31 1.04 1.46 �0.86 11.49 17.44

Jun. 3, 1967 �4.02 0.27 0.04 4.83 3.55 4.67

Jul. 1, 1968 �10.57 �2.62 2.22 �9.39 �23.26 �43.61

Feb. 6, 1969 �15.08 �1.68 — �11.17 �11.07 �39.00

May 29, 1969 �16.56 �0.83 — �0.33 �0.16 �17.89

Jun. 10, 1969 �5.09 �1.70 — �5.39 �1.11 �13.30

Jun. 19, 1969 �3.06 0.39 — 0.91 6.51 4.75

Nov. 6, 1969 4.30 0.29 — �0.77 3.50 7.31

Dec. 13, 1969 �0.61 �1.57 — 0.46 3.97 2.25

Mar. 4, 1970 �1.07 1.98 — 0.46 �24.07 �22.70

Apr. 2, 1970 �0.63 1.01 — �15.15 �5.74 �20.51

Nov. 13, 1970 �3.12 �1.11 — 4.70 6.62 7.09

Dec. 16, 1970 �0.12 1.65 — 1.93 1.27 4.74

Jan. 2, 1971 �0.72 0.81 — �3.02 �34.09 �37.03

Total �84.63 �5.08 �2.32 �43.16 �136.17 �271.39

Note: Figures correspond to accumulated loss (or gain) in stock value over 3-day window for each event.

12. A second-stage analysis is a common approach toidentifying factors that explain variation in wealth effects;see, for example, Harris and Ravenscraft (1991), Blonigenet al. (2004), and Desai and Hines (2004). To the best ofthese authors’ knowledge, however, this approach has notbeen used in previous studies of antismoking policies.


characteristics help explain the estimatedwealth effects. The model is given by:

CARij ¼ d̂ij¼ c1 þ c2MSij þ c3ASRij

þ c4PERFILTij þ c5PERTVj

þ c6ISRij þ uij;

ð2Þ

where d̂ij is the ith firm’s estimated cumulativeabnormal return for event j, MSij is the ithfirm’s market share corresponding to the yearthat event j occurred,ASRij is the ith firm’s ad-vertising-to-sales ratio in the year that event joccurred, PERFILTij is the percentage of firmi’s sales that are filter-tip cigarettes for the yearthat event j occurred, PERTVj is the industrypercentage of advertising devoted to TV andradio during the year that event j occurred,ISRij is the ith firm’s tobacco leaf inventory-to-sales ratio for the year corresponding toevent j, and uij is a zero mean error term.13

Several estimation issues regarding equa-tion (2) need mentioning. First, three versionsof the model are estimated. Model 1 poolsthe positive and negative abnormal returns,whereas models 2 and 3 only use the negativeand positive abnormal returns, respectively, asthe dependent variable. Accordingly, resultsfrom the estimation of models 2 and 3 allowthe authors to identify differences in firm reac-tions depending on whether the abnormal

return is positive or negative. Second, becausethe data are pooled over the five firms, theauthors estimate a fixed-effects version ofequation (2) by including firm dummy varia-bles to account for time-invariant and unob-served firm characteristics.14 Third, becausethe dependent variable in equation (2) is esti-mated in equation (1), this introduces a heter-oscedasticity problem in the second-stageregression (see Blonigen and Wilson, 1999;Harris and Ravenscraft, 1991; Saxonhouse,1976). To control for this, the analysis usesweighted least squares to estimate equation(2), where each observation of the dependentand independent variables is weighted by theinverse of the standard error of the estimatedcoefficient d̂ij.

Expectations on how firm-specific variablescan affect cumulative abnormal returns aresummarized in Table 2. First, bigger firms(higher MS) with higher market power maybe better insulated from the effects of anti-smoking regulations, thereby experiencingsmaller abnormal returns in absolute value;or it could be that bigger firms are bigger tar-gets, having larger absolute abnormal returns.Second, firms that advertise more are par-ticularly sensitive to antiadvertising events.Hence, increases in the advertising-to-salesratio may magnify the abnormal returns.

TABLE 5

Second-Stage Estimation Results: Explaining Variation in Wealth Effects fromAntismoking Policies Initiated between 1964 and 1971

VariableModel 1: PooledAbnormal Returns

Model 2: NegativeAbnormal Returns

Model 3: PositiveAbnormal Returns

Constant �4.43 (3.92) �0.35 (0.33) 3.56b (1.37)

Market share (MS) �0.46 (6.41) �2.52a (0.36) 1.36 (5.39)

Advertising-to-sales ratio (ASR) 35.05 (25.68) �5.11b (2.33) 19.33b (7.53)

Percent filter cigarettes (PERFILT) 7.53c (3.95) 1.68a (0.11) 0.67 (1.70)

Percent TV and radio advertisements (PERTV) �0.05 (0.04) �0.01c (0.004) �0.05a (0.10)

Tobacco leaf inventory-to-sales ratio (ISR) 1.49 (2.38) �0.26 (0.27) �2.71a (0.71)

Firm effects included Yes No No

Sample size 104 60 44

R2 0.32 0.98 0.90

F-statistic 2.90 510.00 49.65

Note: a, b, and c denote significance at the 1%, 5%, and 10%, respectively. Robust standard errors in parentheses.

13. Data on the percentage of advertising devoted toTV and radio is only available at the industry-level.

14. Due to violations of the rank condition, however,the authors were not able to include firm dummy variablesinmodels 2 and 3. Also, across all threemodels the authorsinitially included year dummy variables but found them tobe individually and jointly insignificant. Hence, they wereexcluded from the reported results.


Alternatively, it may be that firms that adver-tise more have greater brand loyalty, whichallows them to more easily ride the antismok-ing wave (i.e., abnormal returns are damp-ened). Third, because filtered cigarettes wereviewed as being less harmful, firms with higherfilter-tip sales should be less affected by anti-smoking events. Fourth, because advertising-related events eventually led to the eliminationof advertising on TV and radio, a higher per-centage of advertising expenditures devotedto broadcast media should intensify the ab-normal return. Finally, firms with higher leafinventories may be more susceptible to anti-smoking policies that result in demand reduc-tions, because this will leave the firm withpossible unwanted inventories.

V ESTIMATION RESULTS

With the exception of Lorrilard, equation(1) was estimated for each firm for the 2,331trading days spanning the period betweenOctober 30, 1962, and March 8, 1972.15 Thissample period includes 300 trading daysbefore the first and after the last event date,respectively—an extension that allows thesample to include trading days that are rela-tively unaffected by the events of interest.16

A. First-Stage Estimation Results

First-stage estimates of the abnormalreturns for the five cigarette producers (i.e.,d̂ij) are presented in Table 3. The authors beginwith a discussion of the three-day event win-dow results. As expected, the Surgeon Gener-al’s Report negatively affected the industry.With respect to the Code of Advertising,one might expect that its adoption would bea positive event for the industry, because itspurpose was likely to dissuade outside regula-tion of advertising content. However, the esti-mated negative wealth effects do not support

this. Rather, it may be that investors viewedthe code as an indication of future advertisingregulation. Largely negative wealth effects arealso associated with early events correspond-ing to the warning labels legislation (i.e., June25, 1964, through June 17, 1965), possiblyreflecting investor concerns over the detrimen-tal impact of warning labels. Yet it is interest-ing to note that for later events (i.e., June 23,1965, through January 2, 1966) the abnormalreturns become positive. As mentioned earlier,one possible explanation for this is that as thelegislation moved closer to becoming law, itincorporated restraints on future regulation(particularly limits on the FTC’s authority toregulate the industry), which investors viewedin a positive light.17

Events surrounding the Fairness Doctrineare on average associated with positive wealtheffects, although these effects across firms aremixed. In contrast, events surrounding the ad-vertising ban tended to have a negative impacton returns in the cigarette industry, with sig-nificance most pronounced during the earlystages of the legislation effort.18 Such a findingis consistent with the anticompetitive view ofadvertising in that the reduction in advertisingfollowing the ban was expected to reduceprofit (due to the increase in competition).

Table 3 also reports results from themonth-long event window, which has been widelyused in previous literature. A comparison ofthe 3-day and 31-day results reveals that eventhough CARs estimated with the 31-day win-dow remain largely negative, their significancedrops off appreciably. Indeed, across all 104firm-event abnormal returns, only 21 are sig-nificantly different from zero at conventionallevels, which is not surprising given the in-creased noisiness of longer event windows.In addition, interpretation of the 31-day

15. Loews acquired Lorillard in 1968, and hence theauthors do not have abnormal returns for Lorillard after1968.

16. The data used to estimate equations (1) and (2)came from various sources. Data on stock returns wereobtained from the Center for Research in Security Pricesdatabase. With respect to the second-stage regression,data on MS and PERFILT came from Maxwell (1998).Data on ASR and ISR came from Standard and Poor’sIndustry Surveys (various years). Finally, data on PERTVwere collected from the Federal Trade Commission(1998).

17. Similar to Mitchell andMulherin’s (1988) analysisof the advertising ban, by proposing to self-regulate ciga-rette advertising via the code, the industry may have‘‘finessed’’ Congress into passing a warning labels bill thatleft advertising untouched. Also, Weissman’s (1964)memo states that he was initially in favor of mild warninglabels ‘‘to thwart the efforts of various states.’’ Further-more, according to SenatorMoss at the time, the final ver-sion of the law was merely a warning ‘‘in whispered tones’’(NewYork Times, July 14, 1965, p. 23). All of these suggestthe ill effects of the warning labels legislation were muchaccounted for early on.

18. This result is similar to Lamdin (1999), who alsofinds negative wealth effects strongest for early events as-sociated with the advertising ban. Yet such results contra-dict Mitchell and Mulherin (1988).


results has two important shortcomings. First,extending the event window (from 3 to 31days) increases the probability of capturingstock price movements unrelated to an-nouncement of antismoking policies (such asannouncement of dividend payouts, earningsreports, etc.). Second, the 31-day window re-sults aggregate the wealth effects from anti-smoking events that took place within thesame month, making it difficult to drawconclusions about the relative importance ofindividual policies.

To gain further insight into the relative im-pact of antismoking policies, the authors com-pute average CARs (denoted l), across allfirms and all events corresponding to eachpolicy. Ranking these according to size, theauthors find that the advertising ban had thelargest negative impact on the industry (l ¼�0.38), followed by the Code of Advertising(l ¼ �0.34), then the 1964 Surgeon General’sReport (l¼�0.23),andlast theadventofwarn-ing labels (l¼�0.19); whereas the average ab-normal return for the Fairness Doctrine wasmarginally positive (l ¼ 0.08).19 Finally, itshouldbenoted that theaverageCARsare sub-stantially lowerwhencomputedusingamonth-long event window.

As further indication of the impact of thesepolicies on shareholder wealth, Table 4 reportsfor each of the 23 events the dollar amountlost/gained by each firm and the industryas a whole. On average, industry losses weregreatest for early antismoking events corre-sponding to the imposition of warning labelsand the advertising ban. Across firms, the ac-cumulated loss in market value was greatestfor R.J. Reynolds (–$136.2 million), followedby American Brands (–$84.63 million), PhilipMorris (–$43.16 million), Liggett (–$5.08 mil-lion), and last, Lorillard (–$2.32 million). Suchvariation is not too surprising, however, giventhat R.J. Reynolds, American Brands, andPhilip Morris were the three largest cigaretteproducers at the time, with the market sharesof Liggett and Lorillard being much smaller.Nonetheless, this does highlight the impor-tance of accounting for differences in stockprice reaction across the firms. Overall, cumu-lative losses to the industry from antismoking

policies initiated in the period 1964 to 1971amounted to $217 million, which translates in-to approximately $1.5 billion when convertedinto 2004 dollars.

B. Second-Stage Estimation Results

Equation (2) was estimated using the 3-dayCARs, and the results are reported in Table 5.With respect to Model 1, which pools the pos-itive and negative CARs, little can be said ofthe importance of firm characteristics inexplaining differences in wealth effects acrossfirms. In this model, the R2 is relatively lowand PERFILT is the only variable with a mar-ginal degree of significance. Perhaps this is dueto the positive and negative abnormal returnsoffsetting each other somewhat, making it dif-ficult to reach any conclusions. Yet when thepositive and negative abnormal returns areseparated, results from the estimation ofMod-els 2 and 3 show substantial improvement.20

For instance, with respect to the negative ab-normal returns, the authors find larger firms(higher MS) who aggressively advertised(higher ASR), particularly on TV and radio(higher PERTV), were most sensitive to anti-smoking events (i.e., the abnormal return waslarger in absolute value).21 Also, firms withhigher filter-tip sales were less affected by an-tismoking policies. Such results are consistentwith several of the authors’ earlier predictionsin that (1) bigger firms were bigger targets ofantismoking campaigns, (2) firms that adver-tised much were more sensitive to antismoking(particularly antiadvertising) policies, (3) anti-smoking policies geared toward the ban of ad-vertising on TV and radio were most harmfulwhen the share of broadcast advertising washigh, and (4) higher filter-tip sales somewhatinsulated firms from the detrimental effectsof the antismoking campaign.

Finally, firm characteristics remain impor-tant in model 3, although explanatory power islower, where the CARs tend to be larger forfirms that aggressively advertised, devoted less

19. To place these averages in context, the value of�0.38 corresponding to the average abnormal return forthe advertising ban clarifies that the events surroundingthe advertising ban led to an average per-event abnormaldrop in stock prices of slightly over one-third of 1%.

20. The authors also estimated model 1 with an inter-acting dummy variable accounting for the negative returns(i.e., dummy variable equals one if return is negative, zeroif positive). In this case, theR2 increased to 0.85. However,because there are sizable differences between the resultsfor models 2 and 3, the authors chose to report those sep-arately in Table 5.

21. Consistent with the results in Table 4, therefore,stock values for the larger firms fell substantially morein response to the antismoking policies.


advertising to TV and radio, and carriedsmaller stocks of tobacco leaf.

VI CONCLUSIONS

Studies of the effects of antismoking poli-cies on the cigarette industry have largelyfocused on the demand side of the marketwith only a handful of papers analyzing wealtheffects from such policies mostly usingmonthly security price data. In this article,the authors identify 23 dates associated withantismoking policies initiated in the period1964–71. Using daily data on stock returnsof five U.S. publicly traded cigarette pro-ducers, the authors conduct a two-stage anal-ysis of the impact of antismoking policies oncigarette producers. In the first stage the anal-ysis estimates the wealth effects associatedwith health warnings and regulatory changes,and in the second stage the authors conducta firm-level analysis of factors that explainthe magnitude of wealth effects across tobaccocompanies.

In addition to finding significant abnormalreturns at both the firm and industry level, theempirical results reveal significant variation inthe wealth effects from the various types ofpolicies. Indeed, the authors find that on aver-age, the advertising ban had the largest nega-tive impact on the industry, followed by theCode of Advertising, then the 1964 SurgeonGeneral’s Report, and finally, the advent ofwarning labels. In contrast, the average cu-mulative abnormal return for the FairnessDoctrine was marginally positive. Further in-vestigation into differences in wealth effectsacross firms reveals that losses in market valuewere higher for the larger firms who advertisedaggressively, particularly on TV and radio.Overall, the authors estimate that antismokingregulatory policies initiated in the period1964–71 had a predominantly negative im-pact on the cigarette industry with inflation-adjusted industry losses in market value andshareholder wealth amounting to approxi-mately $1.5 billion.

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Settling the Smoke: Public Policy and Shareholder Wealth in the Cigarette Industry

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