Information, Marketing, and Pricing
`in the U.S. Antiulcer Drug Market
`
`By ERNST R. BERNDT, LINDA Bui, DAVII) R. REILEY, AND CILEN L. URBAN*
`
`Introduced into the United States in 1977,
`Tagamet was the pioneer product
`in the
`class of antiulcer drugs known as H3-
`antagonists. By promoting ulcer healing
`through inhibiting acid secretion. Tagamet
`was able to heal ulcers and treat prc-ulcer
`conditions pharmacologically on an outpa-
`tient basis,
`thereby substituting for more
`costly hospital admissions and surgeries. In
`1983 another H2-antagonist called Zantac
`entered. and by early 1987 U.S. Zantac sales
`surpassed those of the pioneering Tagamet.
`Today there are four H_,-antagonists sold in
`the United States: Tagamet. Zantac. Pep-
`Cid, and Axid. Zantac is now the world‘s
`largest selling prescription drug. having esti-
`mated worldwide sales in 1994 of about $4
`billion. Each of the four H3-antagonists is
`among the top l0()
`in world drug sales,
`although Tagamet lost U.S. patent protec-
`tion on May 17, 1994.
`In this paper we examine empirically the
`role of information in facilitating and ex-
`plaining growth of the overall antiulcer drug
`market. as well as in shaping the changing
`market shares of the four patented prod-
`ucts. The dissemination of information is
`
`due largely to the use of marketing chan-
`nels. such as visits by manufacturers‘ repre-
`sentatives to physicians (called "detailing").
`
`*Berndt. Sloan School of Management. Mas-
`sachusetts Institute of Technology (Iimbiidge. MA
`02142; Bui: Department of Economics. Boston Univer-
`sity, 270 Bay State Road. Boston. MA 02215, Reiley
`Department of Lconomics, Massachusetts Institute ot
`Technology; Urban: Sloan School ol Management.
`Massachusetts Institute of Technology Financial sup-
`port from the Alfred P. Sloan Foundation I\ gratefully
`acknowledged, as is data support
`liom Stephen (‘
`Chappell of [MS International. J. Stanley Hull oi (‘ilaxo
`Pharmaceuticals, Ditas Riad of Merck & (‘o. and
`William Moore ol Lowe & Partners/SMS. Any views
`and opinions expressed here are attributable only to
`the authors.
`
`[(10
`
`journals, and most
`advertising in medical
`recently. by direct—to-consumer advertising.
`We examine these and also explore pricing
`policies, product differentiation, and order-
`of—entry effects.
`
`I. Background
`
`There are two cost conditions that have
`
`considerable bearing on the structure and
`behavior of
`the pharmaceutical
`industry.
`First, sunk costs are very large. In particu-
`lar. the costs of bringing a product to mar-
`ket
`(doing basic research, winning patent
`approval. engaging in development, per-
`forming clinical
`trials. and obtaining final
`approval from the Food and Drug Adminis-
`tration [FDA]) are currently estimated at
`about $360 million per drug. Second. for
`most
`traditional pharmaceutical products,
`the marginal costs of manufacturing are very
`small. Although appropriate cost data are
`not publicly available,
`it
`is not uncommon
`for generic drugs to sell at 25-30 percent ()f
`the pre-patent-expiration price.
`Informal
`discussions with industry oflicials suggest
`that for the Hfantagonists. production costs
`are about 10-25 percent of the price.
`These cost conditions have implications
`for pricing. Patent protection gives firms the
`ability to influence price, and to the extent
`one is willing to use the Lerner markup
`relation as a pricing rule of thumb, one
`would expect price and marginal—cost condi-
`tions to approximate (P —MC)/P = -1/Sp,
`where .-‘D
`is
`the demand price elasticity.
`With manufacturing costs at 10-25 percent
`of price (markups 75-90 percent). the im-
`plied demand price elasticity would range
`from - 1.1 to -1.3. However, elasticities of
`that size contrast with the common percep-
`tion that demand for prescription drugs is
`extremely price inelastic. Peter Temin (1980
`Ch. 5). for example. notes that physicians
`
`Page 1 of 7
`
`Copyright © 2001 All Rights Reserved
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`

`
`VOL. 85 N() 2
`
`lNF()RM/l’1‘ION, EDU( )4 TING. AND M-1RKETlN(} IN III-5-11. IH (‘~1RI:
`
`10/
`
`traditionally have been relatively unaware
`of drug prices. Other observers have sug-
`gested that moral hazard in the form of
`third—party (insurance) payment practices
`also contributes to low price responsiveness.
`Very little econometric evidence on demand
`elasticities for drugs is‘ available.
`in part
`because the traditional consumer demand
`
`paradigm (utility maximization. marginal
`rates of
`substitution equal
`to relative
`marginal prices. etc.) cannot be expected to
`describe behavior adequately in a market in
`which principal-agent problems (stemming
`from relationships among physicians. pa-
`tients. and insurers) are widespread.‘ In this
`paper we report elasticity estimates viewed
`from the vantage of the firm. not the “con-
`sumer“—whoever that may be.
`Since marginal production costs are small,
`enhancing revenues is essentially the same
`as increasing profits. and thus drug firms
`face strong incentives to shift out
`the de-
`mand curves. Thus it
`is not surprising that
`marketing—sales ratios are quite high in the
`pharmaceutical
`industry. The largest com-
`ponent
`(7()—8() percent) of marketing has
`traditionally involved detailing to physi-
`cians;
`it consists of a company representa-
`tive providing as much product information
`as possible to physicians. given the typical
`short time of the visit (3-10 minutes) and
`the content regulation enforced by the FDA.
`Medical journal advertising is also carried
`out but is less extensive than detailing. Fi-
`nally.
`in the last few years. pharmaceutical
`companies have increasingly employed di-
`rect—to-Consumer
`advertising in various
`media.
`
`The information content of marketing ef-
`forts deals primarily with product differen-
`tiation and nonprice aspects.
`In the H3-
`antagonists market,
`five quality attributes
`are of particular importance: First. the var-
`ious Hfantagonists are viewed as being
`roughly similar in efficacy (the four- to six-
`
`iSee. however. Michael Baye et al. (1994)
`‘For more extensive discussion. see Beindt et al.
`(1994).
`
`week treatment healing rate is about 70-80
`percent
`for duodenal ulcer patients), al-
`though there is some evidence suggesting
`that Zantac has a significantly lower relapse
`rate than does Tagamct for patients on duo-
`denal maintenance treated :it recommended
`
`(lough ct al.. 1984).
`dosages (see K. R.
`Second.
`less frequent dosages are thought
`to enhance patient compliance. When Zan-
`tac entered the U.S. market
`in 1983.
`its
`twice-daily dosing frequency was considered
`more favorable than the regimen of four
`times a day recommended for Tagamet.
`Tagamet responded with a twice—a—day ver-
`sion in late W84. after which considerable
`
`rivalry ensued; today all four H 3-antagonists
`have a once-a-day version. A third quality
`attribute involves adverse interactions with
`
`other drugs. Here Tagamet has been on the
`defensive.
`for early on it was discovered
`that Tagamet interacted with the liver and
`kidney system in a way that could afiiect the
`metabolism of other drugs. As of 1994.
`Tagamet had reported to the FDA signifi-
`cant drug interactions with ten other drugs.
`whereas Zantac and Axid had only one re-
`ported drug interaction. and Pepcid had
`none. A fourth quality characteristic in-
`volves side effects. Here again Tagamet has
`been somewhat on the defensive. for condi-
`tions such as inental confusion in the el-
`
`derly and gynecomastia (breast swelling) for
`males are apparently not as prevalent with
`Zantac. Pepcid. and Axid. Finally. the four
`products compete in terms of medical con-
`ditions (indications) for which the FDA has
`granted treatment
`approval. Although
`Tagamet was the first
`to win approval for
`the treatment of duodenal ulcers. duodenal
`ulcer maintenance. and gastric ulcers.
`in
`1986 Zantac was the first to obtain approval
`for gastroesophageal
`reflux
`disease
`(GERD). a rather common condition that
`ranges from modest heartburn and acid in-
`digestion to being a very serious condition.
`The FDA permits marketing only for ap-
`proved indications. Although Tagamet ob-
`tained FDA approval
`for GERD in 1991.
`and even though Tagamet had very similar
`effects to Zantac. suggesting that
`it would
`likely also be effective in treating GERD.
`not
`having FDA approval
`for GERD
`
`Page 2 of 7
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`

`
`I02
`
`AEA PAPERS AND PROCEEDINGS
`
`MAY 1995
`
`(whereas Zantac did) may have constituted
`a significant marketplace disadvantage for
`Tagamet.
`In terms of pricing, at entry Zantac was
`priced at an 80-percent premium over Taga-
`met, but by May 1994 this premium had
`gradually declined to 19 percent.
`In May
`1994, the price per day's treatment (to drug
`stores) was $2.61 for Zantac, $2.56 for Axid.
`$2.30 for Tagamet, and $2.17 for Pepcid;
`quantity shares for the four products were
`49 percent, 12 percent. 22 percent, and 17
`percent, respectively.
`To understand the roles of marketing,
`pricing, and quality attributes in explaining
`the growth and changing composition of the
`Hfantagonist market, we now outline an
`econometric model
`first
`for
`the H3-
`antagonist industry as a whole, and then for
`the market shares garnered by the four H3-
`antagonist drugs.
`
`II. An Econometric Model
`
`of the H2-Antagonist Market
`
`At the industry level, we expect the quan-
`tity demanded (number of patient days of
`duodenal—ulcer therapy) to depend on price
`per treatment day, various marketing ef-
`forts, and quality attributes. Since market-
`ing efforts provide long-lived information, it
`is
`important
`that cumulative information
`stocks be distinguished from current—period
`new information flows. Define the cumula-
`
`tive marketing information stock S, at end
`of month t as
`
`(1)
`
`SI:(]—5)Sf'l+FI
`I
`
`: Z (1~5)TFr—r
`7:0
`
`advertising (DCA).3 It is worth noting that
`the DCA efforts for Hyantagonists did not
`mention any drug by name, but only encour-
`aged viewers
`to seek advice from their
`physician if they experience heartburn and
`acid indigestion.
`Although such DCA advertising is plausi-
`bly intended to augment overall
`industry
`demand, when two or more products exist,
`marketing efforts are often only focused on
`a particular brand. During its monopoly era,
`Tagamet recouped all
`the benefits of its
`marketing efforts (it had 100 percent market
`share)“ However, once Zantac entered,
`even though rivalry between Tagamet and
`Zantac was
`intense,
`some of Tagamet’s
`marketing efforts might have spilled over to
`the benefit of Zantac, and vice versa. Simi-
`larly, once Pepcid and Axid entered, while
`marketing efforts were typically focused on
`specific brands. spillovers to Zantac and
`Tagamet might have occurred. To allow for
`marketing spillovers
`affecting industry
`(rather than just product-specific) demand,
`we define the effective industry marketing
`stock
`as a weighted sum of the market-
`ing information stocks originally formed in
`various market structures:
`
`(2)
`
`Si* = ILLISII + #252: + #353: + #454:
`
`is the surviving marketing infor-
`where S1,
`mation stock at end of month t that origi-
`nally accumulated in the Tagamet monopoly
`era, S2,
`is the similar stock formed during
`the Tagamet—Zantac duopoly, S3,
`is
`that
`from the Tagamet-Zantac—Pepcid triopoly,
`and S4,
`is that from the Tagamet-Zantac-
`Pepcid—Axid rivalry. Since in a monopoly all
`marketing efforts affect
`industry demand,
`
`is the flow of new marketing infor-
`where F,
`mation efforts during month t, and 5 is the
`monthly depreciation rate. Since 5 is un-
`known, we estimate it econometrically.
`In
`terms of marketing efforts, we distinguish
`three channels: the minutes of detailing to
`physicians (DET), the number of pages of
`medical—journal advertising (PJL), and the
`target rating points of direct-to—consumer
`
`‘Target rating points are defined as the target reach
`(the percentage of the over—age-35 population who
`view the message over the course of the ad campaign)
`times the frequency. where frequency is the number of
`times the average target individual views the message.
`For further discussion, see Philip Kotler (1991 pp
`o(lo—8). The proprietary DCA data were kindly pro-
`vided us by Lowe & Partners/SMS in cooperation with
`Glaxo. Inc
`‘The discussion that follows is based in large part
`on Berndt et al. (1994).
`
`Page 3 of 7
`
`Copyright © 2001 All Rights Reserved
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`

`
`VOL 85 N0 2
`
`Ir\/FORMA Tl()N. EDl’(‘A TING, AND M4RKETING IN lflf./1l.’l'Il ( 4R1,
`
`103
`
`we normalize the p.’s by setting ,u.l = 1.
`Several interesting hypotheses involve the
`us First, if the effectiveness of firms‘ mar-
`keting on mdustry sales is independent of
`market
`structure,
`then M3 = ,u-, ‘= #4 = .
`Second,
`if in the presence of competition
`marketing efforts only affect market shares
`and have a zcro—sum impact on industry
`demand.
`then ,u2 = /1.; 2 ,u4 = 0. Finally.
`if
`the
`industry sales-augmenting effects of
`firms‘ marketing decline as the number of
`products in the industry increases, then 1 >
`M: > M: > #4 > 0.
`For our industry demand equation. we
`specify a log-log model, where Q,
`is quan-
`tity, P1
`is CP1—def1ated price, Dl:'l',*. PJL’*,‘,
`and DCA”: are the effective industry stocks
`defined in (1) and (2), and DGERD is a
`dummy variable taking on the value of 1
`following FDA approval for GERD:
`
`and
`pages marketing stocks. LNDTJ1
`LNJPJ1; the number of adverse drug inter-
`actions for product j relative to Tagamet.
`I-NlNTJ1;“ a discrete variable. DSGERD,
`indicating whether product j has a GERD
`indication advantage relative to Tagamet (1,
`advantage; (1. no advantage; — 1. disadvan-
`tage); aii order-of-entry variable. ENTRY,
`taking on the value of 3 for all Zantac
`observations, 3 for Pcpcid. and 4 for Axid;
`and an AGE variable indicating the number
`of months product j has been in the mar-
`ketplace. Again. an instrumental—variable
`procedure is employed to allow for simul-
`taneity.
`Our data sources are described more fully
`in Berndt et al. (1994).7 The direct—to-con-
`sumer marketing data are for a campaign
`begun by Glaxo (the manufacturer of Zan-
`tac) in June 1992. and they extend through
`May 1994.
`
`(3) mo, : 5,, + fi1LNP,+ fi3LNDE'1‘,“
`
`III. Econometric Results
`
`4. ;5.LNi>iL*7 + ,84LND(‘A*;
`
`+ p,DGi3RD, +
`
`Since the effective industry marketing stocks
`depend nonlinearly on the ,u.’s and 5’s. and
`since marketing efforts. pricing. and quan-
`tity demanded are likely to be jointly deter-
`mined (see Richard Sclimalensee 1972), we
`estimate parameters in equation (3) by non-
`linear two—stagc least squares (NL-2Sl.S).l
`Our econometric model of market shares
`
`in specifying
`(1986)
`follows Urban et al.
`variables relative to the incumbent (Taga-
`met).
`In particular, using a log-log frame-
`work, we specify that
`in month I. demand
`quantities of product j relative to the in-
`cumbent [ln(Q,/Q1)E LNQJ1,
`1
`- Zantac,
`Pepcid, Axid] depend on:
`relative prices.
`LNPRJ1;
`relative detailing and journal-
`
`‘As instruments. we employ the producer price in-
`dex for intermediate goods. production worker wages
`in the pharmaceutical
`industry. cumulative marketing
`efforts by the four companies on l'l()I‘l-ll»-'dI1ttlg0fllSt
`products for each of the three instruments. and time
`
`Based on 201 monthly observations from
`September 1977 through May 1994. we esti-
`mated parameters of equation (3) for the
`industry using NL—2SLS. To be parsimo-
`nious in parameters, we constrained the ,u‘s
`and 5‘s to be the same for the DET and
`
`PJL marketing stocks, but allowed 8 to dif-
`fer for DCA. The preferred model was cho-
`sen based on the lowest value of the tradi-
`tional NL-2Sl.S residual criterion function.
`
`industry
`estimated Hyantagonist
`Our
`price elasticity is —().689 (1 =3.80). while
`elasticity estimates for the DET. PJL, and
`DCA surviving stocks are 0.553 (t = 7.52).
`0.198 (r = 2.79) and 0.008 (1 = 2.67).“ Hence,
`industry demand is positively affected by all
`three of the firms’ marketing channels, but
`DET is most effective; the sum of the three
`marketing elasticities
`is H.759, suggesting
`decreasing returns to scale.
`In terms of
`
`"To accommodate zeros. 1.0 is added to both the
`
`D(;A and the INT variables
`(1994) data base
`Here we extend the Berndt ei al
`to May 1994. Data on prices. quantities, detailing. and
`iournal pages are from IMS lnternational
`"The equation R3 is 1) 99*. and the Durbin-Watson
`statistic is I 912.
`
`Page 4 of 7
`
`
`“Copyright © 2001 All Rights‘ Reserved
`
`Page 4 of 7
`
`

`
`[04
`
`AEA PAPERS AND PROCEEIJINGS
`
`MA Y /995
`
`spillovers, the estimates of 11.2, 113, and 114
`are 0.601 (t=6.59), 0.924 (t:5.30). and
`0.410 (I = 4.00); these us are jointly signif-
`icantly different from 1. and from zero, indi-
`cating that marketing spillovers occur and
`that
`the effectiveness of firms‘ marketing
`efforts on industry sales depends on market
`structure. The extent
`to which spillovers
`occur, however. does not decline monotoni-
`cally with the number of products in the
`market. The DGERD dummy variable co-
`efiicient
`is 0.104 (I = 3.20),
`indicating that
`FDA approval for GERD increased the size
`of the H3-antagonist market by about 10
`percent. Finally.
`the NL—2SLS criterion
`function is optimized at the point where 8
`for the DET and PJL stocks is 0.00, while
`that for the DCA stock is 0.15 (t =0.20).
`implying an annual DCA deterioration rate
`of about 80 percent. Although we are some-
`what surprised that the information stocks
`of DCA and PJL marketing do not depreci-
`ate at all. we note that a similar 5:0
`finding in the context of R&D knowledge
`stocks has been reported by Zvi Griliches
`and Frank Lichtenberg (1984).
`Turning now to econometric results based
`on the market—share model. we obtained the
`following NL~2SLS results. based on 291
`monthly observations:
`
`LNQJ1, = —0.427ENTRY — 0.667 LNPRJI,
`(44.01))
`(8.95)
`
`—« 0.649 LNDTJ 1, + 0. I67 LNJPJ1
`(19.77)
`(1)31)
`
`—« 0.052 DSGERD, 4 11.252 LNlNT.I1
`(2.17)
`(9.01))
`
`+
`
`0.0l0AGE
`(16.65)
`
`with an R2 of 0.983. Order—of-entry effects
`are negative and strong. implying significant
`first-mover advantages. consistent with evi-
`dence from other markets (see Urban et al.,
`1986). The within-H 2-antagonist price-elas-
`ticity estimate is -0.67 and significant. while
`coefficients on relative stocks of detailing
`(0.649) and journal pages of advertising
`(0.167) are positive and significant. The esti-
`
`the
`mated monthly depreciation rate for
`DET and PJL stocks is 0.030 (I : 13.77).
`implying that relative information market-
`ing stocks deteriorate at about 30 percent
`per year. With respect to quality variables.
`the DSGERD coefficient is 0.05. while that
`
`is
`on relative adverse drug llllCl"tlCIl0l'l§
`40.25. suggesting that Tagamet’s market
`share was significantly negatively atllected by
`its disadvantages in terms of GERD and
`adverse drug interactions
`in
`the H 3-
`antagonist market. Finally.
`the age coc-fli-
`cient
`is positive and significant,
`implying
`that, ceteris paribus.
`longevity in the in1i1«
`ketplace positively affects market shares."
`
`IV. Concluding Remarks
`
`We have reported results on llictors al-
`feeting the growth and composition of the
`H3—antagonist drug market. With an H_,-
`antagonist
`industry own-price elasticity of
`—0.69 and between-drug price elasticities
`of
`-0.66.
`the implicit brand—specific own-
`price elasticities in May 1994 are —~0.80 for
`Tagamet (SE : 0.08). / l.03 (Sli : 0.12) for
`Zantac.
`(0.76 (SE : 0.08) for Pepcid, and
`-0.74 (SE : 0.08)
`for Axid. Except
`lor
`Zantae,
`these elasticity estimates are still
`slightly smaller than the —— 1.1 to —— 1.3 val-
`ues one might expect based on the Lerner
`markup rule of
`thumb: nevertheless they
`are not far from 1. and clearly differ lrom 0.
`It is worth noting that when marketing vari-
`ables are omitted from the I‘Cl2ill\'C—(1ClTlil1]Ll
`
`equations. price—elasticity estimates fall
`about half these values.
`
`to
`
`We find that marketing information stocks
`positively affect sales. that the sales elastic-
`ity is largest for detailing. followed by jour-
`nal pages ot advertising. and is smallest lor
`direct-to-consumer advertisiiig. Marketing
`information appears to display overall de-
`creasing returns to scale. We also find that
`
`UAlthough DCA is arguably intended to allect ind1is—
`try demand rather than market shares when the DC‘/\
`information variable is added. shares ol Tagairiet
`iiiid
`Axid were positively Lll'lL‘Clt3(l relative to those of '/an
`tac and Pepeid.
`
`Page 5 of 7
`
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`
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`
`l’()I. 85 .’V() 2
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`I/W‘()RM./1 TI()N_ I:[)('( 4l‘li\/(1, AA/[)M/lRI\'lCI‘1i\(: Ii\ H1211] H1 ('~’lR1
`
`[(15
`
`order—ot'—entry effects are significant, as are
`quality attributes.
`
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`.S'(‘iwzc'c.
`.lune
`1986. 32(6).
`pp. 645-5”.
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