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JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS VOL. 27, NO. 2, JUNE 1992

The Tylenol Incident, Ensuing Regulation, andStock Prices

Thomas D. Dowdell, Suresh Govindaraj, and Prem C. Jain*

Abstract

The much publicized Tylenol incident in 1982 led to stringent packaging regulations forover-the-counter pharmaceutical drugs. The sudden incident and the swift progressionof associated events offer a unique opportunity to assess the wealth effects of the resul-tant regulations. The market value of common stock of Johnson & Johnson, makers ofTylenol, declined by approximately 29 percent, amounting to $2.31 billion. Althoughother firms in the industry also suffered significantly, their share price decline did notoccur around the Tylenol incident but occurred around the subsequent packaging reg-ulation proceedings. On average, 28 other pharmaceutical firms analyzed in this studyexperienced a decline of $310 million per firm, or a total of about $8.68 billion. Theresults suggest that the regulation had a significant negative effect on the common stockprices of firms in the pharmaceutical industry.

I. Introduction

Measuring the effects of regulatioti is of interest to many researchers and toregulatory bodies. Empirical tools of modem finance theory are being appliedincreasingly to study the economic effect of regulation (e.g., Schwert (1981),Dann and James (1982), Spiller (1983), and Jarrell and Peltzman (1985)). Unan-ticipated changes in regulation may cause a concurrent change in security prices,that is, positive or negative abnormal retums. The main difficulty with measur-mg the effects of regulatory change on security prices is identifying when themarket first anticipates the effects of the change. Brown and Wamer ((1985),p. 12) suggest that there is a substantial improvement in the power of statisticaltests to more precise pinpointing of an event.

On October 1, 1982, The Wall Street Journal and the New York Times re-ported deaths of five Chicago suburban dwellers from consumption of cyanide-laced extra strength Tylenol capsules. The makers of Tylenol, Johnson & John-son, immediately recalled two shipment lots and also recalled all extra strengthTylenol capsules from the Chicago area. Later, the company recalled Tylenol

'The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, The Leonard NStem School of Business, New York University, New York, NY 10003, and A. B. Freeman Schoolof Business, Tularie University, New Orleans, LA 70118-5669, respectively. The authors thankJFQA Managmg Editor Paul Malatesta and JFQA referee Robert Bowen for helpful comments.

283

284 Journal of Financial and Quantitative Analysis

capsules nationwide following public panic. Throughout the incident, the com-pany maintained that the bottles had been tampered with outside the factorypremises. The Food and Drug Administration (FDA) initiated investigationsand imposed new stringent packaging regulations for pharmaceutical productssoon after the Tylenol incident. Since the new regulations were unanticipatedand were imposed in a short period of one month, this offers an unusual oppor-tunity to assess the effects of those regulations.

We examine the effects of the Tylenol incident and the subsequent packag-ing regulations on the Johnson & Johnson common stock price and the pricesof common stocks of other firms in the pharmaceutical industry. The resultsshow that the Tylenol incident and the subsequent packaging regulations had a

. statistically significant negative impact on the stock prices of Johnson & John-son and also on other firms in the pharmaceutical industry. The entire period ofstudy is divided into two subperiods. During the initial subperiod of 9 businessdays, only Johnson & Johnson experienced large negative abnormal retums.Other firms in the industry were essentially unaffected. However, in the sec-ond subperiod of 19 business days, when stringent regulations were deliberatedand imposed, other firms were significantly negatively affected. This suggeststhat price changes for the other firms were caused by the packaging regulationsrather than the Tylenol incident per se.

To ascertain whether firms manufacturing drugs competing with Tylenolbenefited from the poisoning event, we analyze the stock price effect on thesefirms separately. For the entire event period, firms competing with Tylenol alsoexperienced negative stock price reactions; however, the effect on them was lessthan the effect on other pharmaceutical firms not competing with Tylenol. Thissuggests that firms competing with Tylenol experienced some relative benefitsfrom the anticipated reduced competition.

The rest of the paper is organized as follows. Section II presents all thehypotheses for examining the data. Section III describes the data and researchmethods used to test the hypotheses. Section IV presents the results followedby conclusions in Section V.

II. Hypotheses for the Various Portfolios of Firms

A. Sample of Firms

We decided to examine only pharmaceutical firms for possible effects. Thisis justifiable since Tylenol-related events would affect primarily, if not exclu-sively, the pharmaceutical industry. The SIC code 2834 represents pharmaceu-tical firms and our sample consists of firms in this group. Table 1 presents a listof 28 firms (other than Johnson & Johnson) included in the sample for whichdata were available on the CRSP tape.' This group is designated as the AllPharma (AP) group.

'The list of pharmaceutical firms was obtained from the Directory of Corporate Affiliationpublished by the National Register Publishing Company, a MacMillan Inc. Co. For our analysis.

Dowdell, Govindaraj, and Jain 285

TABLE 1

Pharmaceutical Firms Selected for Analysis of the Tylenol Incident

Firms in Pharmaceutical Firms in Pain-Kiilerindustry (All Pharma) Group* Piiarmaceuticai (PKP) Group"

1. Abbott Laboratories 1. American Cyanamid2. American Cyanamid 2. American Home Products3. American Home Products 3. Bristol-Myers4. Baxter-Travenol 4. Marion Laboratories5. Bristoi-Myers 5, Revion6. Carter-Wallace 6. Richardson-Vici<s7. Dow Chemicais 7. Rorer Group8. iCN Pharmaceutical 8. Scfiering-Plough9. Key Pharmaceuticai 9. Smith-Kline-Beckmann

10. Lee Pharmaceuticai 10. Sterling11. Liliy (Eli) Company 11. Warner-Lambert12. Mario^n Laboratories Firms in Non-PKP Group

14. 3M 1. Abbott Laboratories15. Pfizer 2. Baxter-Travenol16. Proctor & Gamble 3. Carter-Wailace17. Revion 4. Dow Chemicais18. Richardson-Vici<s 5. iCN Pharmaceuticai19. Robins (A.H.) inc. 6. Key Pharmaceutical20. Rorer Group 7. Lee Pharmaceutical21. Schering Plough 8. Liily (Eii) Company22. Searie 9. Merck23. Smith-Kline-Beckmann 10. 3M24. Squibb 11. Pfizer25.Sterling 12. Proctor & Gamble26. Syntex 13. Robins (A.H.) Inc.27. Upjohn 14. Searie28. Warner Lambert 15. Squibb

16. Syntex17. Upjohn

* This iist consists of firms from the pharmaceuticai industry (SIC 2834) or the parents of the pharma-ceuticai firms.

** This is a subgroup from the All Pharma group, which had products directly competing with the Tyienolbrand name.

A subset of the All Pharma firms was classified as the Pain-Killer Pharma-ceutical (PKP) group, which included those firms that competed directly againstthe Tylenol brand name. This list was prepared from studying the reports bySimmons Market Research Bureau Incorporated, which provided the names ofvarious drugs manufactured by firms in the pharmaceutical industry. Tylenol is anonprescription or over-the-counter drug and it was necessary for the competingbrand names to have similar characteristics. Although Tylenol does not containaspirin, all aspirin-based products directly compete in the same market segment.Tylenol is also a product often taken for cold-related symptoms. Thus, the PKPgroup consists of firms that compete for market share in over-the-counter painkillers and cold-related medicines. Firms not included in the PKP group are in-cluded in the Non-Pain-Killer Pharmaceutical (Non-PKP) group. The objectiveof this classification is to study the differential stock price effects, if any, on thefirms competing with Tylenol, and on other pharmaceutical firms.

stock retums of subsidiaries are used if they are listed on the New York Stock Exchange or theAmerican Stock Exchange. If not, the parent company's stock retums are used. For example.Proctor and Gambles' stock retums are used since its subsidiary Norwich-Eaton does not tradeindependently.


We divide the entire event period of 28 business days into 2 subperiods.The first subperiod is designated as the "Tylenol incident subperiod" (or simplythe initial period). It consists of the initial 9 days of the entire event period. Dur-ing this subperiod, there is little tangible evidence of forthcoming govemmentintervention and thus the stock price reactions are more likely to be influencedby competitive market forces. The main purpose in examining stock retums dur-ing this initial period is to understand the immediate effects of the incident. Thelatter subperiod of 19 days is designated as the "regulatory subperiod" whereevents pertaining to the anticipated packaging regulation are expected to have adominant effect on stock prices.^

B. Hypotheses

The Tylenol incident, in itself, directly affected only the Johnson & JohnsonCompany; however, other pharmaceutical firms were also affected because theywere competing in the same market or because of regulatory effects or both.Appendix 1 presents a chronological summary of the Tylenol incident and thesubsequent news items published in The Wall Street Journal and the New YorkTimes. About 8 to 10 days after the incident, it appeared that there would benew regulations for drug packaging, especially for drugs sold over-the-counter.The new regulations were finally announced 25 business days after the Tylenolincident. Thus, the Tylenol incident and the announcement of new regulationswere inextricably linked.

Schwert (1981) suggests that security price behavior can provide importantevidence about regulatory effects. The main difficulty with measuring the effectsof regulatory change on security prices is identifying when the market firstanticipates the effects. Since the Tylenol incident was completely unanticipatedand regulations followed in a matter of weeks, it is more likely that stock pricechanges were caused by the Tylenol aftermath rather than by other unrelatedevents.-'

An examination of the stock price pattem of Johnson & Johnson indi-cates the severity of the incident.'' Stock prices are affected by an incident ofthis nature because of potential effects on future cash flows of the firms due tochanging demand for their products, and because of possible penalties that could

^As discussed later, the results are unaffected when we allow the Tylenol incident subperiod tovary between 8 and 10 days, and, correspondingly, its complement—the regulatory subperiod—tovary between 18 and 20 days.

'Although there are other news items about the pharmaceutical industry during the event period,the dominating news is clearly that of the Tylenol incident. For example. The Wall Street Journal(October 8, 1982) reports that aspirin in recent weeks has been associated with Reye's syndrome;however, even this information has been previously known.

••Many economists have applied the empirical research methods based on security prices tostudy the economic effects of regulation. For example. Smith, Bradley, and Jarrell (1986) look atstock market data to study oil price regulation, and Spiller (1983) examines the impact of the airlineregulation on individual firms and markets. The arguments of Jarrell and Peltzman (1985) suggestthat the capital market penalizes producers of both recalled drugs and automobiles far in excess ofdirect costs.


be imposed by the regulatory bodies.^ The study of other firms in the industryis interesting since these firms could either benefit or lose because of the in-cident. If the other firms are expected to gain market share at the expense ofJohnson & Johnson, then their stock prices would go up. On the other hand,if the other firms also suffer because of the possible decline in the overall de-mand for over-the-counter pain-killers, or because of costs imposed by expectedpackaging regulations, the industry as a whole would suffer. It is possible thatthe regulations are even beneficial to the industry by imposing barriers to entry.If so, the stock prices of the firrns in the industry would increase to reflect thisexpectation. Our empirical analysis addresses these issues.^

In the absence of some benefits to firms competing with Tylenol, both thePKP and the Non-PKP firm groups would experience similar abnormal retums.If there are benefits to the PKP group because of reduced competition fromTylenol, their stock prices should go up in comparison with other firms in theindustry. One way to study the effects of changes in competitive structureis to examine the differences in the abnormal retums experienced by these twogroups of firms. Common effects across the entire industry (including regulatorymeasures, copycat poisoning incidents, etc.) are not likely to influence theanalysis of differences in abnormal retums. Assuming some benefits accrue tothe PKP group, we expect that their stock prices will not be affected as muchas those of the Non-PKP group.

C. The Tylenol Incident Subperiod Hypotheses

The first hypothesis (hypothesis Hjj, where subscript"/" denotes the Tylenolincident subperiod) presented below is to examine the overall effect on the phar-maceutical industry. It is presented in a two-tailed altemate form that highlightsthe earlier argument that the overall effect on the entire industry could be in anydirection.

To the extent that the PKP group of firms benefits from the reduced com-petition and the transfer of Tylenol market share, these firms should be affectedless negatively (or more positively) than firms not in the same market segment(Non-PKP group). The overall effect on the pharmaceutical industry (AP group)during the Tylenol incident subperiod includes both competitive as well as an-ticipated regulatory and other common effects, but regardless of how the entirepharmaceutical group (AP group) is affected, the differential effect (see //,,2below) should indicate the competitive effect. The hypothesis is presented inthe altemate form to emphasize the anticipated competitive benefits from the

5 Brown, Castanias, and Daley (1983) and Hill and Schneeweis (1983) examine stock retums ofpublic utility firms around the Three Mile Island nuclear accident to assess the importance of theaccident. They study the effects on the firm owning the facilities as well as on the other electricutilities to study the intra-industry effects. Our approach is similar, but we are able to link thenegative effect on the industry stock prices to the regulatory action. We also estimate the dollarvalue of the impact.

*The degree of monopoly or competition in the pharmaceutical industry has been discussedextensively. Comanor (1986) reviews the extant literature on the issue, and reviews claims andcounter-claims made by various authors. He also discusses the costs and benefits of regulation. Heconcludes that the recent literature on the effects of new regulations is one-sided in the sense thatthere is wide acceptance of general depressing effects of regulation.


Tylenol incident.'' Formally, for this Tylenol incident subperiod, the followingtwo hypotheses are tested:

Hii: During the Tylenol incident subperiod /, there was a significant effecton the stock prices of all pharmaceutical firms other than Johnson &Johnson (AP group); i.e.,

CAR,,ap ^ 0.0.

HiX- During the Tylenol incident subperiod /, the effect on the stock pricesof the pharmaceutical firms directly competing with the Tylenol brand(PKP group) was significantly greater than the effect on the phar-maceutical firms not competing with the Tylenol brand (Non-PKPgroup); i.e.,

CAR;,pkp - CAR,,npkp > 0.0.

D. The Regulatory Subperiod Hypotheses

The subperiod subsequent to the Tylenol incident subperiod is designatedas the regulatory subperiod. If the regulatory measures impose additional costson the pharmaceutical firms, then all the firms, on average, would be affectednegatively. On the other hand, if regulation is helpful in restoring consumerconfidence or increasing barriers to entry, the firms would benefit from regula-tion. In effect, abnormal retums to all the firms in the sample are examined inthe regulatory subperiod to study the overall effects of regulation (hypothesisHr,i, where subscript "r".represents the regulatory subperiod). It should be rec-ognized that competitive effects could continue to exist in the second subperiodand we can examine such effects in a manner similar to the previous case byexamining the differential effects on the PKP group and the Non-PKP group.Formally, the following two hypotheses (Wr.i and Wr,2) that are tested are similarto those for the Tylenol incident subperiod (//,,i and //,,2), except for the timeinterval.

Hr,\: During the regulatory subperiod r, there was a significant effect onthe stock prices of all pharmaceutical firms other than Johnson &Johnson (AP group); i.e.,

CAR,,ap ^ 0.0.

HrX- During the regulatory subperiod r, the effect on the stock prices of thepharmaceutical firms directly competing with the Tylenol brand (PKPgroup) was significantly greater than the effect on the pharmaceuticalfirms not competing with the Tylenol brand (Non-PKP group); i.e.,

pkp > 0.0.

'Although not presented as formal hypotheses, we also report differences in abnormal stockretums for Johnson & Johnson and other portfolios for the entire event period as well as for thesubperiods.


III. Research Methodology

The first Tylenol-related death occurred 2 days prior to the October 1, 1982,publication date in both The Wall Street Journal and the New York Times. Theevents more or less culminated with The Wall Street Journal's November 5,1982, report of the new packaging regulations.^ Thus, the period of interest forthis study ranges from 2 days prior to October 1, 1982, to November 5, 1982.

A. Data

Since market knowledge of the Tylenol incident can be identified quiteprecisely in time, daily data are used instead of weekly or monthly data. Thisallows us to better relate stock price changes with specific events. The data on

. daily stock retums were obtained from the daily CRSP (Center for Research inSecurity Prices) tape of the University of Chicago. For each firm in the sample,we also calculated the market value of its common equity by multiplying thenumber of shares outstanding by the price per share before the incident. Thenumber of shares outstanding and the price per share for the firms in the samplewere obtained from the Daily Stock Record publication of the Standard andPoors Corporation. A list of firms included in each category is provided inTable 1.

B. The Model

The market model presented in Equation 1 has been used as the basicframework to study the effect of an incident on stock prices.

(1) . Rj, = aj-^ bjRnu + ep, t=\,....T,

where Rj, is the dividend-adjusted retum on the equity of firm j for day t,Rm, is the equally-weighted CRSP market index,Sj, is the noise or random disturbance term for firm j for day t.

Cov(Rj,,R^,)b, = -^ , and' Var(/?«,,)

aj = E{Rj,)-bjE{R^,).

The parameters aj and bj were estimated using the data from a 120-day periodprior to the event period. A detailed description of this well-known methodologycan be found in Fama (1976) and Brown and Wamer (1980), (1985). Schwert(1981) also outlines this methodology for examining stock returns for testinghypotheses about regulatory effects.

Once the ay and bj had been estimated for firm 7, the daily prediction errors(abnotTnal retums) were calculated by Equation 2,

(2) Uj, = Rj, - {aj + hjRn,,), t > T.

*The announcement about regulation was released on November 4, 1982, by the FDA and wasreported the next day by The Wall Street Journat. The packaging regulations were stringent relativeto existing rules for nonprescription or over-the-counter drugs.


Since the regulatory effects are likely to affect a large number of firmsat the same point in time, Schwert (1981) suggests that the common effectsof regulation on a set of firms can be measured by analyzing the retums toa portfolio of securities.' For each day, the abnormal retums were summedacross firms, to yield daily (equally-weighted) portfolio abnormal retums. AR ,̂is given by

1 ^(3) ARp, - 77 Z " ^ "

Cumulative abnormal retums (CAR) were calculated by summing portfolio ab-normal retums over various time periods. October 1, 1982, is defined as day 0,which is the date the first reports of the Tylenol incident appeared in The WallStreet Journal and the New York Times. Since the newspaper publications weretwo days behind the first recorded incident, the period of interest for this studybegins on day - 2 . CAR from day c to day d are given by

d

(4) CARp = Y.^^P'-t=c

To test the statistical significance of various portfolio abnormal retums andcumulative abnormal retums, r-statistics were calculated as explained in Brownand Wamer (1985).'"

Besides examining whether the firms experienced significant abnormal re-tums around the events, regulators and others should also be interested in mea-suring the effect in dollar terms. The magnitude of the event's effect can bemeasured by computing the change in the market value of the firm's commonequity in dollar terms. For firm j , the effect on its market value on day t, AVy,,,is given by

(5) AKy, = Vj,.yUj,,

where Vj,-\ represents the market value of the firm's common stocks outstandingat the end of day f- 1. For periods longer than one day, and for a portfolioof firms, the effect is computed with cumulation over an appropriate number ofdays and firms.

IV. Results

Tables 2 through 5 present the results of the analysis for Johnson & JohnsonCompany, the All Pharma (Pharmaceutical Industry) sample, the PKP sample,and the Non-PKP sample, respectively. Average portfolio abnormal retums, t-statistics, and CAR are presented from event day -120 through event day -1-120.

'Schwert (1981) also points out some important statistical reasons for using portfolio retumsinstead of analyzing the retums on each individual security. Among others, the portfolio retumdirectly incorporates the cross-sectional dependence of its components. Collins and Dent (1984)provide some evidence on this issue.

'"The exact statistical testing procedure is described in detail in Jain (1985).


TABLE 2

Johnson & Johnson: Effect of the Tylenol Incident on the Common Stock Prices of Johnson & Johnson

Date

820413820511820609820708820805820902820917820929820930821001821004821005821006821007821008821011821012821013821014821015821018821019821020821021821022821025821026821027821028821029821101821102'821103821104821105821112821129821228830125830223830323

Day

-120-100-80-60-40-20-10- 2-1

0123456789

1011121314151617181920212223242530406080

100120

AbnormalReturn

-1.94%-0.19

1.500.70

-0.18-0.50-0.31-0.81-6.02

0.63-5.72-6.26

4.42-5.74

3.75-0.79-4.99-2.33-0.43-2.52

3.930.04

-3.012.24

-2.71-1.96

3.06-2.39

0.312.920.41

-3.08-1.66

0.06-0.26

0.150.26

-1.312.340.090.46

f-Stat

-1.25-0.12

0.970.05

-0.12-0.32-0.20-0.52-3.89***

0.41-3.70***-4.04***

2.85***- 3 . 7 1 * * *

2.42*'-0.51-3.22***-1.50-0.28-1.63

2.54**0.03

-1.95*1.45

-1.75*-1.26-T:S8**

-1.540.201.89*0.26

-1.99**-1.07

0.04-0.17

0.100.17

-0.841.510.060.30

CumulativeAverageReturn

-1.94-4.88

1.430.65

-2.23-9.02

-12.34-10.99-17.01-16.38-22.10-28.36-23.94-29.68-25.93-26.72-31.71-34.03-34.47-36.99-33.06-33.02-36.03-33.79-36.50-38.46-35.40-37.78-37.47-34.55-34.15-37.23-38.89-38.83-39.10-37.20-36.90-23.26-26.95-36.42-35.24

Selected Cumulative Abnormal Returns and Value Changes

From

-120-2-226

Days

To

- 31

25120

CAR

-10.18%-11.92-28.92

3.85

/-Stat

-0.61-3.85***-3.53***

0.26

CumulativeEffect on

Market Value ofCommon Stocks ($ Millions)

-192.0-492.7

97.32.5

-298.0-955.9

-1254.1-1139.4-1666.7-1614.6-2088.0-2576.5-2253.3-2691.9-2422.0-2481.0-2850.5-3014.2-3044.0-3216.9-2954.5-2951.8-3160.8-3009.9-3196.2-3327.3-3126.6-3288.0-3267.5-3074.1-3046.4-3257.6-3367.4-3363.8-3381.0-3261.0-3249.3-2306.8-2604.3-3271.2-3213.5

Cumulative per

($ Millions)

-1068.1-1019.9-2313.0

167.6

Day 0: Day of the Tylenol incident announcement in The Wall Street Journal.*: One, two, and three stars represent significance at 10 percent, 5 percent, and 1 percent, respec-

tively (two-tailed test).


In addition, the average change in dollar value for each portfolio is computed andthen cumulated through day -1-120. Finally, for selected subperiods, cumulativeabnormal retums and their f-statistics are presented in the tables.

A. Effects on Johnson & Johnson

Table 2 shows that Johnson & Johnson experienced highly significantnegative abnormal retums of —28.92 percent during the entire event period(days - 2 through 4-25). The stock prices fell by 11.92 percent in 4 days surround-ing the event, i.e., from day - 2 to day -i-l. During the next 7 days, there wassome public panic, a nationwide recall of Tylenol capsules, and an inquiryby the Food and Drug Administration (FDA) into the Tylenol incident (seeAppendix 1). The entire event period impact of -28.92 percent translates into$2,313 million for Johnson & Johnson stockholders.

We also report abnormal retums for the period around the event period.The firm shows some decline in the stock retums during the pre-event period,which appears to be random since it is statistically insignificant. Also, since theTylenol incident was completely unanticipated, any stock price movement priorto the incident cannot be attributed to it. The period after day -i-25 shows nosignificant movement in the stock price of the firm.

B. Effects on the Pharmaceutical Industry (All Pharma) Sample

Table 3 reports the results of the All Pharma portfolio of 28 firms. Over-all, the firms experienced statistically significant negative abnormal retums of-11.83 percent for the event period from day - 2 to day H-25. It is important tonote that in the 4 days including and following the incident itself, the industrydid not experience a significant effect. For day —2 to day -i-l, the abnormalretum is only -0.68 percent, which is statistically insignificant in contrast withthe large negative abnormal retum of -11.92 percent experienced by Johnson &Johnson. In effect, there is essentially no reaction for the All Pharma portfoliountil day -i-6, although Johnson & Johnson had experienced about —16 percentabnormal retums by this time. While the results show that the pharmaceuticalindustry experienced negative abnotTnal retums for the entire event period, theeffect was not due to the Tylenol incident per se." In dollar terms, the averageloss to a firm was $310 million amounting to a total in excess of $8 billion.

C. Effects on the PKP Group and the Non-PKP Group

Table 4 reports the results of the PKP portfolio of 11 firms. These firmsexperienced significant negative abnormal retums of—6.17 percent for the eventperiod from day —2 to day +25. This portfolio did not experience significantabnormal retums around the Tylenol incident per se. On average, a firm in thePKP group experienced a loss of $229 million over the entire event period.

"Although most of the news items during the regulatory subperiod relate to regulatory dis-cussions, the news on October 27, 1982, reports that poi.son is also found in Anacin. Thus, wereplicated the entire analysis by deleting days 18 and 19 from the event period and the conclusionsare unaffected.


TABLE 3

All Pharma Sample (28 Firms): Effect of the Tylenol Incident on the Common Stock Prices of All Firmsin the Pharmaceutical Industry except Johnson & Johnson

Date

820413820511820609820708820805820902820917820929820930821001821004821005821006821007821008821011821012821013821014821015821018821019821020821021821022821025821026821027821028821029821101821102821103821104821105821112821129821228830125830223830323

Day

-120-100-80-60-40-20-10- 2- 1

0123456789

1011121314151617181920212223242530406080

100120

AbnormalReturn

0.20%-0.31

0.270.24

-0.270.08

-0.82-0.39-0.65

0.65-0.30-0.20

1.130.160.05

-1.34-0.81-1.53-1.79-0.21-0.43

0.240.26

-0.62-1.80

0.550.53

-0.38-1.40-0.11-0.39-0.68-0.63-0.94-0.79-0.78

0.33-0.36

0.370.870.54

/-Stat

0.35-0.55

0.490.43

-0.480.14

-1.47-0.69-1.15

1.16-0.53-0.36

2.00**0.290.08

-2.38**-1.45-2.73***-3.19***-0.38-0.76

0.440.46

-1.09- 3 . 2 1 * * *

0.970.94

-0.67-2.49**-0.19-0.70-1.22-1.12-1.67*-1.41-1.39

0.58-0.65

0.661.550.97


0.20-2.71-0.59-0.73-1.50-3.69-4.52-5.62-6.27-5.62-5.92-6.12-5.00-4.84-4.79-6.13-6.94-8.48

-10.27-10.48-10.91-10.67-10.41-11.02-12.82-12.28-11.75-12.13-13.52-13.63-14.02-14.71-15.34-16.28-17.07-17.00-17.25-16.14-18.16-20.44-16.39


From

-120- 2- 226

Days

To

- 31

25120

CAR

-5.23%-0.68

-11.830.67

/-Stat

-0.86-0.61-3.98***

0.12

Cumulative per FirmEffect on

Market Value ofCommon Stocks ($ Miliions)

-4.6-109.8-77.4

-104.0-117.3-147.1-156.0-169.5-192.1-170.9-171.9-180.1-140.9-119.6-114.0-147.8-181.3-227.8-287.3-288.4-295.8-296.9-300.0-311.2-360.7-360.1-336.0-368.1-398.1-407.6-405.5-421.5-417.8-449.5-469.7-530.3-553.4-461.5-536.5-606.9-520.7

Cumulative perFirm Fffprt1 II 1 1 1 ^ _ 1 r \j\j L

($ Millions)

-159.7-12.2

-310.0-51.0




TABLE 4

Pain-Killer Pharmaceutical Sample (11 firms): Effect of the Tylenol Incident on the Common StockPrices of Firms Competing with the Tylenol Brand Name

Date

820413820511820609820708820805820902820917820929820930821001821004821005821006821007821008821011821012821013821014821015821018821019821020821021821022821025821026821027821028821029821101821102821103821104821105821129821213821228830125830223830323

Day

-120-100-80-60-40-20-10- 2- 1

0123456789

1011121314151617181920212223242540506080

100120

AbnormalReturn

-0.31%-0.10

0.430.980.28

-0.15-0.71

0.080.130.77

-0.61-0.34

1.150.880.25

-0.30-1.19-1.21-1.60-0.34

0.100.350.66

-0.25-1.67-1.07-0.04

0.48-1.69

0.040.300.370.62

-1.65-0.39

0.270.26

-0.68-0.14

0.700.38

/-Stat

-0.46-0.15

0.651.470.43

-0.23-1.07

0.120.191.16'

-0.92-0.51

1.73*1.330.38

-0.45-1.79*-1.82*-2 .41 * *-0.51

0.150.530.99

-0.37-2.52**-1.61-0.07

0.73-2.54***

0.060.450.560.94

-2.49**-0.59

0.410.39

-1.02-0.21

1.060.57


-0.31-4.18-2.69-0.56-1.19-1.76-3.94-4.23-4.10-3.32-3.94-4.27-3.13-2.24-1.99-2.29-3.48-4.69-6.29-6.64-6.54-6.19-5.53-5.78-7.45-8.52-8.57-8.08-9.77-9.73-9.43-9.06-8.43

-10.09-10.48-12.09-12.27-11.47-18.10-18.80-10.79


From

-120- 2-226

Days

To

-31

25120

CAR

-4.31%0.37

-6.17-0.31

/-Stat

-0.600.28

-1.76*-0.05

Cumulative per FirmEffect on


-0.3-115.9. -91.7

-79.1,-84.0

-133.9-175.1-195.2-204.5-184.0-188.6-194.8-170.6-141.6-162.1-171.6-221.7-259.6-303.1-306.1-304.8-296.6-301.0-310.5-351.8-370.3-356.4-367.6-400.0-398.5-393.1-391.6-391.0-416.6-428.3-529.5-496.8-470.5-578.0-593.8-457.4

Cumulative perFirm Effect($ Millions)

-199.811.2

-228.5-29.1




TABLE 5

Non-Pain-Killer Pharmaceutical Sample (17 firmsl): Effect of the Tylenol Incident on the Common StockPrices of All Firms in the Pharmaceutical Industry except Johnson & Johnson and Firms Competing

with the Tylenol Brand Name

Date

820413820511820609820708820805820902820917820929820930821001821004821005821006821007821008821011821012821013821014821015821018821019821020821021821022821025821026821027821028821029821101821102821103821104821105821112821129821228830125830223830323

Day

-120-100-80-60-40-20-10- 2- 1

0123456789

1011

. 121314151617181920212223242530406080

100120

AbnormalReturn

0.53%-0.44

0.17-0.24-0.63

0.23-0.90-0.69-1.15

0.57-0.10-0.12

1.11-0.31-0.09-2.01-0.57-1.74-1.91-0.13-0.77

0.180.00

-0.85-1.89

1.590.90

-0.93-1.21-0.21-0.84-1.37-1.44-0.47-1.05-0.17

0.36-0.16

0.700.980.65

/-Stat

0.82-0.69

0.27-0.37-0.98

0.35-1.39-1.07-1.78*

0.88-0.15-0.18

1.72*-0.48-0.13- 3 . 1 1 * * *-0.89-2.70***-2.96***-0.20-1.19

0.270.00

-1.32-2.92***

2.47**1.39

-1.45-1.87*-0.32-1.30-2.12**-2.24**-0.73-1.62-0.27

0.56-0.25

1.081.531.01


0.53-1.75

0.76-0.83-1.70-4.95-4.89-6.52-7.67-7.10-7.20-7.32-6.21-6.51-6.60-8.61-9.18

-10.92-12.84-12.97-13.74-13.56-13.56-14.41-16.30-14.71-13.81-14.74-15.95-16.16-16.99-18.36-19.81-20.28-21.33-19.78-20.58-19.16-18.20-21.50-20.02


From

-120- 2- 226

Days

To

- 31

25120

CAR

-5.83%-1.37

-15.491.31

/-Stat

-0.83-1.06-4.54***

0.21

Cumulative per firmEffect on


-7.4-105.9-68.2

-120.0-138.8-155.7-143.6-152.9-184.1-162.5-161.0-170.5-121.7-105.3-82.9

-132.4-155.1-207.3-277.2-276.9-290.0-297.1-299.4-311.7-336.4-353.5-322.7-368.4-396.9-413.5-413.6-440.8-435.1-470.8-496.5-544.6-568.9-455.7-509.6-615.5-561.6

Cumulative perFirm Fffprt1 11 1 1 1 1̂1 1 u u L

($ Millions)

-133.7-27.3

-362.8-65.1




Table 5 reports the results of the Non-PKP portfolio of 17 firms. For theentire event period, these firms also experienced significantly negative abnormalretums of -15.49 percent. The Non-PKP firms experienced a reduction of$362.8 million per firm during the entire event period.

Overall, for the relatively short event period of 28 business days duringwhich the Tylenol incident took place and the subsequent regulatory measureswere undertaken, the empirical results show that the incident and the packagingregulations had large adverse effects on the pharmaceutical industry, includingthe pharmaceutical firms competing with Tylenol.

D. Tests of Hypotheses Relating to the Competitive Aspect {Hi^ andAV/,2) and the Regulatory Aspects of {Hr,^ and Hr,2)

Ex ante, we consider the period from day - 2 to day 4-6 (a total of 9 days)to be the Tylenol incident subperiod in which to examine the effects of theincident itself on the industry (hypothesis f/,,i) and the competitive effects byexamining the relative abnormal retums across two subgroups (hypothesis //,,2).There is very little discussion of possible regulatory measures during the Tylenolincident subperiod. Most of the discussion and implementation of regulationtakes place in the remaining subperiod. If we consider a very short Tylenolincident subperiod, we would decrease the power of our statistical tests bydownplaying the initial effects of the Tylenol incident. On the other hand, if weinclude too many days in the Tylenol incident subperiod, we would include theregulatory effects into the initial effects. Our conclusions are not affected if weconsider the.Tylenol incident subperiod to be as short as 8 days or as long as10 days.

Various results for the two subperiods are reported in Table 6. During theinitial 9-day subperiod, the abnormal returns of —0.9 percent (Panel A, column2) experienced by the pharmaceutical industry (AP group) as a whole is notsignificant. Therefore, the hypothesis //,,i is not accepted. The results are alsoinsignificant when we consider the Tylenol incident subperiod to be 8 or 10 dayslong. Hypothesis //,,2 examines the difference between the two subgroups offirms while controlling for effects common to the two subgroups during thisperiod. For the Tylenol incident subperiod (Panel A, column 2), the differencebetween the abnormal retums for the PKP group and the Non-PKP group issignificantly positive (4.8 percent, r-statistic of 2.3). We conclude that, for theTylenol incident, the PKP group of firms benefited relative to the Non-PKPgroup. This is consistent with the prediction of the hypothesis W,,2 that themarket anticipated the benefits from the competitive effects of the incident. Theresults are similar when we choose the Tylenol incident subperiod to be 8 or10 days long.

Results for the second set of two hypotheses (A/, j to //,,2) are reportedin Panel B of Table 6. The first four rows show that during the regulatory pe-riod, the pharmaceutical industry, on average, experienced significantly negativeabnormal retums. In this case, the results support hypothesis f/,-.i at any reason-able level of significance. In the Tylenol incident subperiod, as reported earlier.


TABLE 6

Cumulative Abnormal Returns in Percentages for Various Portfolios of Firms around the TylenolIncident (Day 0 is the Tylenol incident announcement in The Wall Street Journat)

Panel A.

Portfolio ofFirms

J&JAPPKPNon-PKP(J&J-AP)(J&J-PKP)(J&J-Non-PKP)(PKP-Non-PKP)

Panel B.


Panel C


- 2 to 5'(1)

-15.8%0.42.3

-0.8-16.2-18.1-15.0

3.1

6 to 25(5)

-13.2%-12.3

-8.5-14.7-0.9-4.7

1.66.2

(-3.6)(0.2)(1.2)

(-0.4)(-4.0)(-4.3)(-4.0)

(1.6)

1

(-1.9)(-4.9)(-2.9)(-5.1)(-0.1)(-0.7)

(0.2)(2.0)

Total EventPeriod (days)

- 2 to 25(9)

-28.9%-11.8-6.2

-15.5-17.1-22.7-13.4

9.3

(-3.5)(-4.0)(-1.8)(-4.5)(-2.2)(-2.9)(-1.7)

(2.6)

Initial Tylenol Incident Periods (days)

- 2 to 6(2)

-16.5-0.9

2.0-2.8

-15.7-18.6-13.8

4.8

(-3.6)(-0.5)

(1.0)(-1.4)(-3.6)(-4.2)(-3.1)

(2.3)

- 2 to 7(3)

-21.5-1.7

0.8-3.4

-19.8-22.4-18.2

4.2

(-4.4)(-1.0)

(0.4)(-1.7)(-4.3)(-4.8)(-3.9)

(1.9)

Subsequent Regulatory Periods (days)

7 to 25(6)

-12.4-10.9

-8.2-12.7

-1.4-4.2

0.44.5

(-1.8)(-4.5)(-2.8)(-4.5)(-0.2)(-0.6)

(0.1)(1.5)

8 to 25(7)

-7.4-10.1-7.0

-12.12.7

-0.44.85.2

(-1.1)(-4.2)(-2.5)(-4.4)

(0.5)(-0.1)

(0.7)(1.8)

- 2 to 8(4)

-23.9-3.2-0.4-5.1

-20.6-23.5-18.8

4.7

(-4.7)(-1.7)(-0.1)(-2.4)(-4.3)(-4.8)(-3.8)

(2.1)

9 to 25(8)

-5.1-8.6-5.8

-10.43.50.75.44.6

(-0.8)(-3.7)(-2.1)(-3.9)

(0.6)(0.1)(0.9)(1.6)

a.J&J:AP;PKP:

Non-PKP:

/-statistics in parentheses,Johnson & Johnson firm.All Pharmaceutical firms (28 firms).Group of 11 firms with products competing with Tylenol (pain-killer pharmaceutical group),andGroup of pharmaceutical firms not competing with Tylenol (17 firms).

Johnson & Johnson stock was affected more negatively than others. However,in the regulatory subperiod, the results indicate that the difference between theabnormal retums to Johnson & Johnson and the abnormal retums to the groupof All Pharma firms (as well as the PKP and the Non-PKP groups) is not signif-icant. In this subperiod, say from day -i-7 to day H-25 (Panel B, column 6),while Johnson & Johnson experienced —12.4-percent abnormal retums, thegroup of 28 firms experienced a similar —10.9 percent. The results are consist-ent with all pharmaceutical firms (including Johnson & Johnson) experiencing


equal negative abnormal retums in excess of —10 percent due to regulatorymeasures.'^

With respect to //, 2, the result is similar to that for / / , j . There is a sig-nificant difference between the abnormal retums experienced by the PKP groupand the abnormal retums experienced by the Non-PKP group. This is consistentwith competitive effects persisting beyond the Tylenol incident subperiod. Sincecompetitive benefits seem to accrue to the PKP group throughout the event pe-riod, a comprehensive assessment of the relative effects on the PKP and theNon-PKP firms can be obtained by examining abnormal retums for the entireperiod from day - 2 to day +25 (Panel C). For this period, Non-PKP firms aresignificantly (9.3 percent, ^-statistic of 2.6) more affected than the PKP firms.

Overall, the results suggest that the new packaging regulations had largeadverse effects on the pharmaceutical industry, including the pharmaceuticalfirms competing with Tylenol.'^ Although other unknown possibilities mayexplain these large effects, our conclusion, in part, is supported by the fact thatthe announcement of the Tylenol incident in itself did not affect the stock pricesof other firms. The pharmaceutical industry as a whole experienced negativeabnormal retums prirnarily during the subperiod in which packaging regulationswere discussed and announced.

V. Conclusions

Following the 1982 Tylenol incident, common stockholders of firms inthe pharmaceutical industry experienced large statistically significant negativeabnormal retums during the introduction of new FDA regulations requiringtamper-proof packaging. The firms in the industry were affected negatively dueto imposition of new packaging regulations and not because of the Tylenolincident per se. Since the packaging regulations followed in a matter of a fewweeks after the Tylenol incident, reliable estimates of their effects could be madeusing data on daily stock retums.

This study examines abnormal retums from the day of the incident to theday when new packaging regulations were announced. During the initial periodof about 9 days, only the manufacturer of Tylenol, Johnson & Johnson, experi-enced significantly negative abnormal retums. Other firms in the industry wereessentially unaffected. In the subsequent period of about 19 days, regulationswere proposed, discussed, and imposed. During this period, other firms in theindustry were also negatively affected. The negative effect on other firms dur-ing this latter period is approximately the same as that on Johnson & Johnson.Since there was no other important event identified during this subperiod, the

'^While it may be possible to think of some reasons that could affect the industry as a whole,reasons other than the regulatory reason appear difficult to justify, primarily because most of thediscussion in the newspapers during this period was about regulation.

"The abnormal retums examined in the above analysis represent the effects experienced by thefirms as a whole; however, many of these firms consist of both pharmaceutical and nonpharmaceu-tical segments. The entire analysis was repeated by examining abnormal retums after weightingindividual firms by the sales of their pharmaceutical products. We obtained the pharmaceuticaldivision sales from the company's annual reports and relied on segment reporting data therein. Theabove-mentioned qualitative conclusions were unaffected.


negative abnormal retums appear to be caused by the anticipated costs relatedto the packaging regulations alone.

The overall impact on the Johnson & Johnson Company's common stockfor the period of interest was —28.9 percent, which is statistically significant andcorresponds to losses in excess of $2.3 billion to its stockholders. The sample of28 firms in the pharmaceutical industry for the entire event period experienced asignificantly negative effect of -11.83 percent, which corresponds to an averageloss of $310 million per firm. The sample of 28 pharmaceutical firms andJohnson & Johnson experienced a combined loss of $11 billion. Consistentwith the arguments of Jarrel and Peltzman (1985), the results suggest that capitalmarkets penalize firms for incidents like the Tylenol case far in excess of directcosts. The excessive costs are indirect costs or costs of goodwill, which havegreater effect on firm value than implied by immediate, unexpected outfiows offunds.

Although both groups of firms are affected significantly negatively, there isevidence that the firms directly competing in the market with the Tylenol prod-ucts were less affected than other pharmaceutical firms. The smaller negativeeffect for these firms could be due to expected benefits from an increase in themarket share of over-the-counter drugs offsetting some of the adverse effects ofthe packaging regulations.

Appendix 1. Chronology of Events Related to the TylenolIncident and Packaging Regulations

Oct. 1, 1982 The Wall Street Journal and the New York Times reportedthe deaths of 5 Chicago suburbanites within the last 2 dayscaused by consuming extra strength Tylenol capsules laced withcyanide.

Johnson & Johnson's McNeil Consumer Products Co. pulledout the Tylenol capsules lot that contained the two bottles withcyanide.

Oct. 4, 1982 Johnson & Johnson and FDA authorities ruled out product con-tamination and declared that cyanide-laced capsules of Tylenolwere planted at individual stores. The company agreed to workclosely with the investigation, to underwrite part of the cost,and to offer a reward for information about the crime. Chicagoauthorities recalled all Tylenol products.

Experts in packaging stated it is possible and beneficial to de-velop tamper-resistant drug packaging. The increased cost wasestimated at 5 to 10 percent.

Oct. 5, 1982 The FDA and the over-the-counter drug industry formed ajoint task force to consider more secure packaging for non-prescription medicine.


Drug retailers appeared to be loyal to Johnson & Johnson Co,It was felt that damage could be controlled if the crime wassolved quickly,

Johnson & Johnson was hit with their first Tylenol-relatedlawsuit,

Oct. 6, 1982 Tylenol containing strychnine was found in Califomia, Johnson& Johnson pulled out al! regular and extra strength Tylenolcapsules from U,S. store shelves.

Fear spread to other brands, but was confined to capsules. An-alysts were increasingly pessimistic about Johnson & Johnson'sability to recover from unfolding events,

Oct. 7, 1982 A Wharton School student's death in April 1982 was linked toTylenol, He was declared the eighth victim of the poisoningsas the death toll in Chicago had risen to 7.

Govemment moved quickly to avert a host of local regulations,each different, that would be difficult for manufacturers to meet.

Secretary Richard Schweiker of the Department of Health andHuman Services proposed ordering the drafting of a federalrequirement for tamper-resistant packaging of over-the-counterdrugs,

Oct, 8, 1982 Johnson & Johnson decided to scrap all capsule forms offeringto exchange tablets for capsules.

Biggest gainers were other acetaminophen products rather thanaspirin. Aspirin in recent weeks has been associated with Reye'ssyndrome.

Tamper-resistant packaging was determined feasible by a U.S.Industry Panel.

Oct, 13, 1982 A survey revealed that Tylenol orders fell 25 percent, but com-petitors lacked enough products to fill the market gap.

Competitors found it difficult to capitalize without appearingmacabre or in poor taste. They have avoided rewriting ads,

Oct, 14, 1982 Chicago police arrested a suspect and the FBI charged RobertRichardson with trying to extort $1 million dollars from Johnson& Johnson's McNeil Co,

Oct, 15, 1982 The Proprietary Association, representing 85 firms that makeover-the-counter medicines, requested that the FDA requiretamper-proof packages,

Oct, 18, 1982 The FDA stated plans to implement rules requiring tamper-resistant packaging in early February,

Oct, 25, 1982 Johnson & Johnson tried advertising on television, promotingthe Tylenol brand name for the first time since the poisoningreports.

Dov^dell, Govindaraj, and Jain 301

Oct, 27, 1982 Protective drug packaging was expected to appear in the weekafter. Reports had been surfacing of other nonprescription drugsthat had been adulterated.

Highest cost was not packaging, but replacing products thatwere not tamper resistant. Poison is found in Anacin MaximumStrength and Excedrin Extra Strength in Denver,

Oct. 29, 1982 Johnson & Johnson reported a $50 million charge to their thirdperiod eamings due to the Tylenol recall,

Johnson & Johnson survey found most people do not know: J&Jexonerated and only capsules, not tablets involved. Consumersadmitted irrational fears remain,

Nov, 5, 1982 The federal govemment announced the packaging regulationsspecifying new over-the-counter drug packaging requirements.

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