THE JOURNAL OF INDUSTRIAL ECONOMICS
`Volume LV
`December 2007
`
`0022-1821
`No. 4
`
`Notes on the Journal of Industrial Economics Website
`www.essex.ac.uk/jindec/
`
`DIRECT TO CONSUMER ADVERTISING AND
`PRESCRIPTION CHOICE
`
`Toshiaki Iizukaw
`Ginger Z. Jinz
`
`This paper examines the effect of direct-to-consumer advertising
`(DTCA) of prescription drugs on doctors’ choice of drug brands. Using
`antihistamines as an example, we show that DTCA has little effect on
`the choice of brand despite the massive DTCA expenditure incurred in
`this class. In contrast, promotional activities directed toward physicians
`have larger and longer lasting effects. These results, together with the
`market-expanding results shown in Iizuka and Jin (2005), suggest that
`DTCA is effective in increasing the aggregate demand per therapeutic
`class but does not affect doctor choice of prescription within a class.
`Therefore, DTCA may be viewed as a public good for all drugs in the
`same class.
`
`Posted 12 June, 2006
`
`wAuthors’ affiliations: Graduate School of International Management, Aoyama Gakuin
`University, Aoyama, Tokyo, Japan.
`e-mail: toshi.iizuka@gmail com
`zDepartment of Economics, University of Maryland & NBER, College Park, Maryland
`20742, U.S.A.
`e-mail: jin@econ. umd. edu
`
`VERTICAL INTEGRATION AND SHARED FACILITIES
`IN UNREGULATED INDUSTRIES
`
`Felipe Balmacedaw
`Eduardo Saavedraz
`
`In this paper we analyze the equilibrium market structure, following
`liberalization, of an industry involving an essential facility. Two
`alternative modes of market entry are considered, in conjunction with
`vertical integration, namely: (i) full entry, which means building a new
`and more efficient facility at a positive fixed cost; and (ii) partial entry,
`which means purchasing existing capacity from the incumbent, at a fixed
`price per unit that is freely negotiated between the incumbent and the
`entrant. We show that vertical integration is a dominant strategy for
`
`r 2007 The Authors. Journal compilation r 2007 Blackwell Publishing Ltd. and the Editorial Board of The Journal of Industrial
`Economics. Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK, and 350 Main Street, Malden,
`MA 02148, USA.
`
`771
`
`Page 1 of 12
`
`ACRUX DDS PTY LTD. et al.
`
`EXHIBIT 1616
`
`IPR Petition for
`
`U.S. Patent No. 7,214,506
`
`

`

`THE JOURNAL OF INDUSTRIAL ECONOMICS
`Volume LV
`December 2007
`
`0022-1821
`No. 4
`
`Notes on the Journal of Industrial Economics Website
`www.essex.ac.uk/jindec/
`
`DIRECT TO CONSUMER ADVERTISING AND
`PRESCRIPTION CHOICE
`
`Toshiaki Iizukaw
`Ginger Z. Jinz
`
`This paper examines the effect of direct-to-consumer advertising
`(DTCA) of prescription drugs on doctors’ choice of drug brands. Using
`antihistamines as an example, we show that DTCA has little effect on
`the choice of brand despite the massive DTCA expenditure incurred in
`this class. In contrast, promotional activities directed toward physicians
`have larger and longer lasting effects. These results, together with the
`market-expanding results shown in Iizuka and Jin (2005), suggest that
`DTCA is effective in increasing the aggregate demand per therapeutic
`class but does not affect doctor choice of prescription within a class.
`Therefore, DTCA may be viewed as a public good for all drugs in the
`same class.
`
`Posted 12 June, 2006
`
`wAuthors’ affiliations: Graduate School of International Management, Aoyama Gakuin
`University, Aoyama, Tokyo, Japan.
`e-mail: toshi.iizuka@gmail com
`zDepartment of Economics, University of Maryland & NBER, College Park, Maryland
`20742, U.S.A.
`e-mail: jin@econ. umd. edu
`
`VERTICAL INTEGRATION AND SHARED FACILITIES
`IN UNREGULATED INDUSTRIES
`
`Felipe Balmacedaw
`Eduardo Saavedraz
`
`In this paper we analyze the equilibrium market structure, following
`liberalization, of an industry involving an essential facility. Two
`alternative modes of market entry are considered, in conjunction with
`vertical integration, namely: (i) full entry, which means building a new
`and more efficient facility at a positive fixed cost; and (ii) partial entry,
`which means purchasing existing capacity from the incumbent, at a fixed
`price per unit that is freely negotiated between the incumbent and the
`entrant. We show that vertical integration is a dominant strategy for
`
`r 2007 The Authors. Journal compilation r 2007 Blackwell Publishing Ltd. and the Editorial Board of The Journal of Industrial
`Economics. Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK, and 350 Main Street, Malden,
`MA 02148, USA.
`
`771
`
`Page 1 of 12
`
`

`

`Copyright of Journal of Industrial Economics is the property of Blackwell Publishing Limited and
`its content may not be copied or emailed to multiple sites or posted to a listsermr without the
`copyright holder's express written permission. However, users may print, download, or email
`articles 'For individual use.
`
`Page 2 of 12
`
`Page 2 of 12
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`

`

`Direct to Consumer Advertising and Prescription Choice ∗
`
`Toshiaki Iizuka† Ginger Z. Jin‡
`
`Abstract
`
`This paper examines the effect of direct-to-consumer advertising (DTCA) of prescription
`
`drugs on doctor choice of drug brands. Using antihistamines as an example, we show that
`
`DTCA has little effect on the choice of brand despite the massive DTCA expenditure incurred
`
`in this class. In contrast, promotional activities directed to physicians have a larger and long-
`
`lasting effects. These results, together with the market-expanding results shown in Iizuka
`
`and Jin (2005), suggest that DTCA is effective in increasing the aggregate demand per
`
`therapeutic class but does not affect doctor choice of prescription within a class. Therefore,
`
`DTCA may be viewed as a public good for all drugs in the same class.
`
`1
`
`Introduction
`
`Traditionally, prescription drugs were marketed towards doctors, and direct-to-consumer adver-
`tising (DTCA) was limited. This is partly because DTCA on TV was prohibitively expensive:
`prior to 1997, any DTCA that contained both brand name and medical claims must disclose a
`“brief summary” of drug effectiveness, side effects, and contraindications. As a result, TV ads
`with these details were long and costly. In August 1997, the Food and Drug Administration
`(FDA) clarified that pharmaceutical firms can advertise brand name and indications on TV
`without a “brief summary.”1 Following the clarification, DTCA expenditures increased substan-
`tially from $800 million in 1996 to $2.5 billion in 2000, which created an intensive debate on the
`
`∗† Graduate School of International Management, Aoyama Gakuin University. Email: toshi.iizuka@gmail.com
`‡Department of Economics, University of Maryland & NBER. Email:
`jin@econ.umd.edu. We wish to thank
`Thomas Hubbard, Scott Stern, Timothy Hannan, Judy Hellerstein, Bill Evans, Seth Sanders, John Rust, Robert
`Maness, and the participants at the NBER 2003 IO meeting for constructive comments and suggestions. Also
`thank Kathryn Aikon for helping us interpret the FDA rules, and Catherine Burt for guiding our usage of the
`NAMCS data. The first author acknowledges grants from Grants-in-Aid for Scientific Research, Japan Society of
`Promotion of Science. All errors remain ours.
`1DTCA still needs to include a “major statement” of the most important risks and refer consumers to other
`
`sources for more comprehensive information.
`
`1
`
`Page 3 of 12
`
`

`

`effects of DTCA. Proponents of DTCA emphasize the educational value of DTCA: many chronic
`diseases are under-diagnosed and under-treated, and DTCA can inform potential patients of the
`existence of such medical treatment. Opponents argue, however, DTCA may mislead patients
`into demanding heavily-advertised drugs, leading to inappropriate drug use and unnecessary
`purchase of expensive drugs.2
`
`To understand the effects of DTCA on pharmaceutical demand, it is useful to consider
`the impact of DTCA in two steps.
`In the first step, DTCA may inform the public of the
`existence of the medication and thereby bring potential patients to the doctor. If true, DTCA
`expands the prescription drug market. Second, after the patient visits the doctor, DTCA may
`affect the doctor’s prescription choice. For example, a patient who watched a TV commercial
`of a prescription drug may persuade the doctor to prescribe the medicine, even if it is not the
`appropriate one. Clearly, proponents of DTCA stress the potential benefit from the first step,
`and opponents of DTCA are concerned about the harm generated by the second step.
`
`Our previous study (Iizuka and Jin, 2005) examined the first step by combining 1994
`– 2000 drug-specific DTCA data with the 1995 – 2000 National Ambulatory Medical Care
`Surveys (NAMCS) and found that higher DTCA expenditures were associated with increased
`doctor visits. This paper contributes to the debate by examining the second step, i.e., whether
`DTCA affects physician prescription choice once the patient visits a doctor. We focus on the
`prescription of non-sedating antihistamines, one of the most heavily DTC-advertised classes.
`Utilizing the data above, we estimate a discrete choice model. We allow advertising to last
`long and estimate depreciation rates for the four types of promotional expenditures – detailing,
`medical journal advertising, free samples, and DTCA (the first three target doctors). We find
`that DTCA has a positive but not statistically significant effect on specific brand choices. In
`contrast, advertising directed to doctors has a much larger and long-lasting effect on the choice
`of prescription. Thus, the primary role of DTCA appears to bring potential patients to the
`doctor but not to affect the choice of prescription.
`
`This paper contributes to the small, but growing literature that analyzes the demand
`effects of DTCA.3 Most of the existing evidence is based on aggregate data. Calfee et al. (2002)
`didn’t find a significant effect of DTCA on new Statin prescriptions or renewals. Rosenthal et al.
`(2003) examined the aggregate sales data of five therapeutic classes and found that DTCA has a
`significant effect on total class sales but not on market shares within each class. Narayanan et al.
`(2004) examined the effect of DTCA and other marketing efforts on allergy prescriptions. They
`found that DTCA increases both category sales and brand share, but the latter effect is weaker
`
`2Both sides of the debate are well documented. See Holmer (1999) and Holmer (2002) for a summary of the
`
`proponent’s position, and Hollon (1999) and Wolfe (2002) for a summary of the opponent’s position.
`3Even a smaller collection of literature exists on the supply side of DTCA. This includes Rosenthal et al.
`
`(2002) and Iizuka (2004).
`
`2
`
`Page 4 of 12
`
`

`

`than that of detailing. While closely related, our paper is different from the above references
`because we use patient-level prescription data rather than aggregated market-level data. The
`advantage of individual data is that we can control for various patient-doctor attributes such as
`age, gender, race, insurance status, and physician specialty. Consumer and doctor heterogeneity
`could drive the effect of advertising on aggregate market shares, but it is difficult for an analysis
`using aggregated data to control for such heterogeneity.
`
`A working paper by Wosinska (2002) is probably the closest to ours. Using individual claim
`data from Blue Cross of California, she examined the impact of DTCA on the prescriptions of
`cholesterol-reducing drugs. She found that DTCA has a small but significant impact on the
`demand if the brand is on the formulary. Aside from examining a different therapeutic class,
`this paper is different from Wosinska’s in two ways: first, we use national representative data and
`control for various patient and doctor characteristics, which is difficult to achieve by using claim
`data from a single insurer. More importantly, we allow four types of promotional expenditures
`to depreciate over time and estimate a separate depreciation rate for each. By this, we can
`distinguish the instant impact of advertising from the effect that lasts over time.
`
`2 Data
`
`We combine individual-level data from NAMCS and brand-level advertising data for non-
`sedating antihistamines from TNS Media Intelligence/ Competitive Media Reporting (CMR)
`and IMS Health. NAMCS provides a national representative sample of individual visits to
`office-based physicians. For each office visit, it includes the month of visit, patient demograph-
`ics, insurance status, physician specialty, and prescription choices, if any.4 Monthly DTCA
`expenditure data from 1994 to 2001 is obtained from CMR. CMR monitors advertising ex-
`penditures in several media, including network TV, cable TV, newspapers, and magazines. In
`addition to DTCA, pharmaceutical firms promote their drugs via professional channels targeting
`physicians. This includes detailing, medical journal advertising, and free samples. From IMS
`Health, we obtain monthly data from July 1997 to December 2001 and annual data from 1994
`to 1997. Our analysis focus on the visits that occurred between July 1997 and December 2001,
`but we use prior advertising data to construct advertising stock (see Section 3 for more detail).
`
`Since NAMCS doesn’t collect information on prescription copays, we obtain corresponding
`monthly wholesale price from IMS Health. We compute patient-day price based on the sales
`dollars and quantity sold for each drug. We note that the wholesale price is not ideal since
`this may poorly correlate with the patient’s copay, but this is the best we can do for a national
`representative sample. Aware of this caveat, we use the wholesale price as a proxy control and
`
`4See Cherry et al. (2001) for more detailed description of NAMCS.
`
`3
`
`Page 5 of 12
`
`

`

`do not interpret its impact as price elasticity. All data are matched by drug names and by the
`month of visit.
`
`The drug class we examine is the non-sedating antihistamines. We focus our attention to
`the second-generation non-sedating antihistamines, i.e., Claritin, Zyrtec, and Allegra. Neither
`generic nor over-the-counter (OTC) versions of these drugs were available during the time period
`of our data.5 Historically, this class also included Seldane and Hismanal. We didn’t include them
`in the data set primarily because we observe them very infrequently. However, we included
`a Hismanal withdrawal dummy in the estimation in order to control for the possibility that
`doctor preference on Claritin, Zyrtec and Allegra might have changed after the withdrawal of
`Hismanal.6 In total, we observe 2,545 patient-visits between July 1997 and 2001, which resulted
`in the prescription of one of the three allergy drugs. These patients correspond to 1,151 unique
`doctor identities, implying that on average we observe 2.21 patient-visits per doctor.
`
`Non-sedating antihistamine is one of the most heavily advertised classes using direct-to-
`consumer channels. However, the use of DTCA differs somewhat among the three drugs, which
`allows us to identify the effects of advertising on the choice of prescription drugs. While Claritin
`is clearly the leader in DTCA expenditures in this class (see Figure 1), this does not appear to
`have helped increase or maintain its market share: Claritin’s market share has declined since
`1997 regardless of the large amount of DTCA (see Figure 2). This suggests that DTCA may
`not have a strong impact on the physician’s prescription choice. Table 1 reports descriptive
`statistics.
`
`3 The Model
`
`If a physician maintains her authority to choose a drug, DTCA may encourage people “to seek
`medical help,” but may not influence brand choice. On the other hand, if DTCA enables patients
`to “persuade” the physician to prescribe what they want, DTCA may have a positive effect on
`brand choice. To distinguish these arguments, we estimate demand for prescription drugs using
`a conditional logit model. Specifically, we assume that physician k chooses a drug j for patient
`i at time t, among J alternatives, which maximizes the objective function represented by Vijkt:
`
`mA(m)
`δx
`j(t−x)]
`
`t−tj0
`
`X x
`
`=0
`=1
`5Claritin switched to the OTC market in 2002, and Clarinex entered the market also in 2002.
`6Although Seldane was not officially withdrawn from the market until February 1998, FDA had proposed
`
`[βm ·
`
`4X m
`
`max
`j∈J
`
`Vijkt =
`
`removing Seldane from the marketplace as early as January 1997 (FDA 1997). Since our analysis starts from
`
`July 1997, we believe the impact of Seldane withdrawal is minimal. Hismanal was introduced to the market in
`
`January 1998 and withdrawn on June 21, 1999. Hismanal withdrawal dummy equals one after July 1999.
`
`4
`
`Page 6 of 12
`
`

`

`+ βX Xjt + βP Pjt + βjY Yit + βjH Hkt + βjW Wt + ekjt + ǫijt.
`
`We distinguish four types of promotional expenditures in Ajt: DTCA, detailing, professional
`journal advertising, and free samples. This allows us to separately examine the impact of
`different promotional devices available for pharmaceutical firms. Because the effect of advertising
`may last more than one month, for each type of advertising, we model the advertising stock as
`a sum of the flows from the month of drug entry (tj0) to the month under study (t) with a
`constant depreciation rate δ per month. We estimate a separate δ for each type of advertising.7
`
`As for the other terms, Xjt is a vector of product characteristics including both drug
`dummies and the age of the drug, Pjt is the wholesale price, Yit is a vector of patient i’s
`attributes including insurance status (i.e. Medicare, Medicaid, private insurance and self-pay)
`and demographics (i.e. age, gender and race), Hkt is a vector of physician k’s specialty (i.e.
`family practice, internal medicine, and all others), and Wt is a dummy set to one after Hismanal
`withdrawal. We allow the effects of Yit, Hkt, and Wt to be drug specific, which effectively
`captures individual heterogeneity.
`
`The error term consists of two parts: ekjt denotes physician k’s unobserved preference for
`drug j, and ǫijt is the logit error independent across patients. We treat ekjt as random effects
`that conform to a normal distribution with an unknown variance.8 In doing so, we capture the
`unobserved correlation among the patients that have seen the same doctor.9
`
`To obtain parameter values, we write down the likelihood function and maximize it by
`using the NLMIXED procedure in SAS. The procedure employs a dual quasi-Newton algorithm
`and adaptive Gaussian quadrature to allow for random effects (by physician identity) in the
`non-linear specification of conditional logit. Since there is a possibility for diminishing returns
`to advertising, we have also tried log(Ajt) or the quadratic term of Ajt instead of linear Ajt.
`Since the main results never change, we only report those with linear Ajt.
`
`There are some concerns that the advertising variables of our interest are endogenous
`in the sense that they could be correlated with aggregate demand shocks such as changes in
`
`7To construct advertising stock, we need data prior to July 1997. For DTCA, we have monthly DTCA
`
`expenditure data between January 1994 and June 1997. For professional advertising variables, we have annual
`
`data between 1994 and 1997 and monthly data after July 1997. To accommodate the seasonal nature of the disease
`
`and corresponding advertising expenditures, we allocate the annual data between 1994 and 1997 to each month
`
`using the observed pattern of advertising spending between 1998 and 2001. Specifically, for each advertising type,
`
`we compute the average expenditure share of each month between 1998-2001 and apply them to 1994-1997 data.
`8Precisely, there are two variance terms, one for Zyrtec and one for Allegra (Claritin is the comparison group).
`9Aside from doctor clustering, one may also be concerned about unobserved correlations within the same drug-
`
`month. To our knowledge, the econometric literature has not provided a universal solution for double clustering,
`
`and we believe doctor-clustering is more appropriate in this context.
`
`5
`
`Page 7 of 12
`
`

`

`demographics. This is particularly problematic in the studies that use aggregate sales data,
`where individual attributes are not observed. Fortunately, micro-level data we use allows us to
`include extensive interactions between patient/doctor characteristics and drug dummies, thus
`alleviating the problem.10 Moreover, advertising expenditures are usually determined three to
`four months ahead of time, so it is less likely that the simultaneity problem (or reverse causality)
`biases the estimates.
`
`4 Empirical Results
`
`We present the results in two tables: Table 2 reports the coefficients by advertising, individual
`characteristics, physician characteristics, and the timing of Hismanal withdrawal. Based on these
`estimates, Table 3 reports the marginal effect of increasing each type of advertising expenditure
`of drug j on the probability of prescribing drug j.
`
`Our main interest lies in the four types of promotional activities. As shown in Table 2,
`the coefficient for DTCA is not statistically different from zero, suggesting that, regardless of
`the massive DTCA campaign on allergy drugs, DTCA has no significant impact on physician’s
`prescription choices.11 This contrasts with the results in Iizuka and Jin (2005) that DTCA
`increases visits to physicians substantially. Interestingly, the coefficients for detailing promotion
`and medical journal advertising are positive and weakly significant, suggesting that prescription
`choices are influenced by the promotional efforts targeted at doctors. These results suggest that
`consumer-directed and professional-directed advertising play quite different roles in promoting
`prescription drugs; while DTCA increases patients visits to doctors, professional advertising
`affects the doctor’s prescription choice. The third type of professional advertising – free samples
`– appears to have little impact on prescription choice.
`
`The marginal effects reported in Table 3 paint a more detailed picture: calculated at the
`sample average (weighted), the marginal effects suggest that increasing drug j’s DTCA by $1
`million in a month has a positive but insignificant effect on the probability of prescribing drug
`j in that month. This result is similar across all the three drugs. In comparison, one-million-
`
`10Some previous studies in this area (e.g., Berndt et al., 1995 and Iizuka and Jin, 2005) have used the instruments
`
`of the type “DTCA for the firm in unrelated classes.” This strategy was difficult to implement in this case
`
`because the correlation between allergy advertising and the same firm’s advertising in other classes is not strong.
`
`For example, Claritin accounts for most of Schering Plough’s advertising budget and this leads to a very weak
`
`correlation between the ads expenditure of Claritin and Schering Plough’s 18 other products.
`11The result that the coefficient on DTCA is insignificant may have two interpretations: first, the true effect is
`
`indistinguishable from zero; second, our estimates are imprecise. Since the data have more variations in DTCA
`
`than in the other advertising variables, we suspect the first interpretation is more plausible.
`
`6
`
`Page 8 of 12
`
`

`

`dollar increase in Claritin detailing would enhance the probability of prescribing Claritin by
`10.9% (with standard error 5.39%). Similarly, one-million-dollar increase of journal advertising
`would lead to significantly more prescriptions in Claritin and Zyrtec, but not Allegra.
`
`The estimated depreciation rates reported in Table 2 suggest that professional advertising
`lasts longer, while DTCA is short-lived. These depreciation rates are estimated precisely except
`that the coefficient on DTCA is not statistically different from zero and the DTCA depreciation
`reaches the lower bound of zero. These estimates suggest, for example, the effect of detail-
`ing reduces to 87.4% after one month and 66.8% after three months, but DTCA depreciates
`completely within a month.12
`
`Since DTCA not only has a weaker impact in the current month, but also depreciates
`much faster in the following months, the overall effect of DTCA is substantially weaker than
`that of professional advertising. To give an example, suppose an average firm increases detailing
`promotion by $1 million in a specific month (at the mean of all observed variables). This
`investment increases the cumulative stock of detailing promotion over the next 12 months by
`$6.36 million.13 If another firm tries to use DTCA to offset the business-stealing effect created
`by the first firm’s detailing, this firm would have to increase DTCA by as much as $14.93 million
`in the same month.14 This shows that the business-stealing effect of DTCA is much weaker than
`that of detailing. The same argument goes for professional journal advertising.
`
`The other coefficients also provide insights into the demand for allergy drugs. The co-
`efficient for price suggests that prescription choices are not sensitive to the wholesale price.
`As noted before, however, this may be because our price measure correlates poorly with the
`patient’s out-of-pocket costs. Dummy variables corresponding to Allegra and Zyrtec are not
`different from zero, suggesting that the efficacy of the three drugs are similar. The interaction
`between Allegra and Hismanal withdrawal dummy is insignificant, suggesting that doctor pref-
`erence between Allegra and Claritin did not change after the withdrawal. The same results hold
`for Zyrtec. Also, estimates suggest that women are less likely to receive Claritin relative to men,
`and older age increases the probability of receiving Allegra relative to Claritin and Zyrtec, in
`order. Relating to insurance status, Claritin is more popular among privately insured patients.
`Within Medicaid patients, Allegra is less preferred relative to Claritin and Zyrtec.15
`
`12In a related research, Berndt et al. (2003) reports that, using aggregate data for antiulcer drugs, the effect of
`
`detailing reduces to 95% after one month.
`131 + 0.874 + 0.8742 + .. + 0.87411 = 6.359.
`14We obtain this result as follows. First, if one spends $1 million in DTCA in a specific month, the cumulative
`
`stock of this DTCA over the next 12 months is still $1 million. Using the coefficients reported in Table 2, we can
`
`compute the necessary DTCA expenditures to offset detailing advertising as 6.359/1 ∗ 0.4425/0.1885 = 14.93.
`15However, we should be cautious because NAMCS collects information on the type of insurance that most
`
`likely covers the visit, not the resulting prescription.
`
`7
`
`Page 9 of 12
`
`

`

`5 Discussion
`
`If we integrate the results shown above and the results from Iizuka and Jin (2005), DTCA seems
`effective in increasing aggregate demand per therapeutic class but does not affect doctor choice of
`prescription within a class. This suggests that the effect of DTCA is primarily market-expanding
`rather than business-stealing. Thus, DTCA may be viewed as a public good for all drugs in the
`same class. In this section, we provide supply-side evidence that supports this argument.
`
`If DTCA is a public good, one may immediately ask why drug firms do not simply free ride
`on others’ advertising efforts. Which companies, if any, have incentives to use DTCA given the
`nature of the advertising? To consider these questions, note that even if the public-good nature
`of DTCA does not allow an advertising company to reap the full benefits, the company may
`still find it worthwhile to advertise if the absolute effect of market-expansion is large enough.
`This suggests that companies with larger market shares are more likely to use DTCA, since they
`benefit more from the increased market size. Moreover, we would also expect that there is a
`cut-off point below which low-market share firms do not advertise at all. In fact, we can obtain
`a crude estimate on this threshold using the results from Iizuka and Jin (2005). In that paper,
`we reported that every $28 DTCA after 1997 would generate one patient visit in the next 12
`months. This implies that if one extra visit generates one prescription, consumer compliance
`rate is 60%, and every filled prescription implies $80 profits for the drug manufacturer, the
`manufacturer will advertise as long as its market share is over 58.3%.16
`
`To check these predictions, we look at all therapeutic classes included in NAMCS and
`examine which firms use DTCA more intensively. Here, we implicitly assume that the public
`good argument applies to all therapeutic classes, although our analysis up to this point focuses on
`antihistamines. The assumption is consistent with the existing evidence that DTCA has a much
`smaller or no impact on prescription choice relative to detailing (e.g., Rosenthal et al. (2003),
`Wosinska (2002)). We match the DTCA data for all prescription drugs obtained from CMR and
`NAMCS by drug name and year of visit. Since NAMCS is a national representative, we use the
`weight it assigns to each individual visit to compute the market share of each prescription drug
`in its drug class, where drug class is defined by the 4-digit National Drug Code as of 2000.
`
`Table 3 tabulates yearly DTCA expenditure by whether a drug’s market share within its
`class was 0-20%, 20-40%, 40-60%, or 60+% in the previous year. Using data covering between
`1995 and 2000, the table shows that drugs with market shares 40% or higher in the previous
`year spent a lot more on DTCA per drug-year than those with smaller market shares. Put more
`crudely, the propensity of DTCA increases dramatically with market share: for drugs with 20%
`or less market shares in year t-1, only 3.7% advertise in year t. In contrast, for drugs with 60+%
`
`16That is, 28/(80 ∗ 0.6) = 0.583.
`
`8
`
`Page 10 of 12
`
`

`

`market shares, 19% advertise. If restricted to the classes that ever used DTCA, a drug with a
`60+% market share on average accounts for 69% of the total DTCA expenditure in its class,
`while a drug with a 20-% market share only contributes 4%.17
`
`This supports the argument that “larger” drugs benefit more from the market-expanding
`effects of DTCA, and therefore have more incentives to advertise than their “smaller” competi-
`tors within the same therapeutic class. Thus, the supply-side evidence is consistent with the
`demand side results that DTCA may be viewed as a public good in a therapeutic class.
`
`6 Conclusion
`
`This paper examines the effect of DTCA on doctor choice of prescription drugs. Using antihis-
`tamines as an example, we show that DTCA has little effect on the choice of brand despite the
`massive DTCA expenditure incurred in this class. In contrast, we find that direct-to-physician
`advertising (i.e., detailing and medical journal advertising) has a larger and long-lasting effect
`on prescription choice. These results, together with the market expanding results shown in
`Iizuka and Jin (2005), suggest that DTCA is effective in increasing the aggregate demand per
`therapeutic class but does not affect doctor choice of prescription within a class. Therefore,
`DTCA may be viewed as a public good for all drugs in the same class. Regarding the debate on
`the effect of DTCA, our results support the view of proponents that DTCA has little impact on
`the choice of prescription once the patient arrives at the physician office. We note, however, that
`welfare implications are far reaching, partly because our model does not consider the potential
`substitution between the antihistamines we examine and other alternatives such as non-drug
`treatments and over-the-counter medications.
`
`7 References
`
`Berndt, E.R., L. Bui, D.R. Reiley, and G.L. Urban [1995]: ”Information, Marketing, and Pricing in the
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`US Antiulcer Drug Market,” American Economic Review, 85(2), pp. 100-105.
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`Berndt, E.R., R.S. Pindyck, and P. Azoulay [2003]: ”Consumption Externalities and Diffusion in Phar-
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`maceutical Markets: Antiulcer Drugs,” Journal of Industrial Economics, 51(2), pp. 243-270.
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`Calfee, J., C. Winston, and R. Stempski [2002]: ”Direct-to-Consumer Advertising and the Demand for
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`Cholesterol-reducing Drugs” Journal of Law and Economics, pp. 673-690.
`
`17All these patterns are robust if we estimate regression models, controlling for drug class fixed effects and
`
`common year dummies.
`
`9
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`Page 11 of 12
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