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- 1. The current issue and full text archive of this journal is available at www.emeraldinsight.com/1750-6123.htm The endogenous modeling of the effect of direct-to-consumer advertising in prescription drugs G.K. Kalyanaram Endogenous modeling of the effect of DTCA 137 GK Associates, New York, New York, USA Abstract Purpose – The purpose of this paper is to study two major research objectives. The ﬁrst objective is to investigate the effect of direct-to-consumer advertising (DTCA) on market share in the pharmaceutical drugs industry by modeling advertising decision of the ﬁrm as an endogenous decision. The second objective is to examine and determine whether there is any empirical support for the argument advanced by medical insurers and providers that DTCA advertising encourages brand switching. Design/methodology/approach – Data on sales, price, DTCA, direct-to-physician advertising (DTPA), and average cost of consumption per usage for three prescription (Rx) drugs categories was obtained for the period, January 1998 to December 1999. A simultaneous model of market share and DTCA is proposed. Market share is modeled as a function of DTCA, price, the intensity of competition as represented by the number of competitive brands, and DTPA. DTCA is modeled as a function of its lagged market share (with the optimal number of lags to be determined empirically), and the average cost per consumption usage. Findings – This paper ﬁnds that there is a positive and signiﬁcant effect of DTCA on market share when advertising decision is modeled as an endogenous decision. The empirical results suggest brand switching by consumers. There is, thus, some evidentiary support for the argument made by the insurance providers. Originality/value – This paper is unique for two reasons. First, the paper estimates the effects of DTCA in a simultaneous model accounting for endogenous decision by the ﬁrm. Therefore, the estimates are unbiased and robust. Second, the paper investigates the important public policy question of the social welfare of DTCA. Keywords Advertising, Medical insurance, Consumption, Pharmaceuticals industry Paper type Research paper Introduction The purpose of this paper is to investigate the effects of direct-to-consumer advertising (DTCA) on market share in the pharmaceutical drugs industry, and examine the arguments regarding social welfare of DTCA. We intend to estimate the effect of DTCA by allowing for endogenous decision by the ﬁrm regarding advertising choices. Researchers have shown that advertising decisions by ﬁrms are endogenous. As such, we consider the treatment of advertising as an endogenous decision. The issue of DTCA has become an important public policy issue. Proponents of direct advertising to consumers argue that DTCA has a market-expanding effect: advertising informs consumers of new or alternate treatment options and, therefore, generates new doctor visits. If true, this could improve patient welfare, because many diseases are under diagnosed and the treatment may be more efﬁcient. According to this proposition, the DTCA effect will be found mainly on quantity demand, and not on marker share. Opponents argue, however, that DTCA raises some important public welfare concerns. One such concern is that the patients may be misled into demanding International Journal of Pharmaceutical and Healthcare Marketing Vol. 3 No. 2, 2009 pp. 137-148 q Emerald Group Publishing Limited 1750-6123 DOI 10.1108/17506120910971713
- 2. IJPHM 3,2 138 heavily advertised drugs, leading to inappropriate drug use and the unnecessary purchase of expensive drugs. According to this proposition, the DTCA effect would be found mainly on market share, and not on quantity demand. Not surprisingly, pharmaceutical ﬁrms support the former position, while insurers and medical providers generally agree with the latter view. In this paper, our research objectives are to model advertising effect as endogenous decision and investigate if there is empirical support for the theory advanced by medical providers and insurers. So empirically, we examine the question: is there a statistically signiﬁcant effect of DTCA on market share when such advertising decision by the ﬁrm is modeled as endogenous? The pharmaceutical drugs product category is different from other product categories in many ways. For example, products in pharmaceutical industry cannot be placed in the market without detailed examination and explicit regulatory approval from the Food and Drug Administration (FDA). The FDA has to issue a New Drug Approval (NDA) order before the product can be placed in the market. The FDA also determines and regulates whether a drug should be prescribed by a physician (Rx) or can go directly over-the-counter (OTC). Finally, the FDA also determines whether a drug should continue to be prescription driven even when it is placed OTC. Second, advertising in the pharmaceutical drugs category is also regulated. Advertising and promotion in pharmaceutical industry come in two forms – direct-to-physician advertising (DTPA), and DTCA. There is a substantial history of regulatory legislation and guidelines with regard to DTCA – the subject of our interest and this study. The ﬁrst important regulatory landmark with regard to advertising is the 1938 Federal Food, Drug and Cosmetic Act. This act made the distinction between prescription (Rx) and OTC drugs, and deﬁned different labeling guidelines for Rx and OTC drugs. The 1962 Kefauver-Harris amendments to the Federal Food, Drug and Cosmetic Act gave the FDA its current responsibility for monitoring Rx drug promotional materials, and set the parameters required for Rx marketing efforts: Rx promotional materials cannot be false or misleading; they must provide a “fair-balance” coverage of risks and beneﬁts of using the drug; they must provide a summary of contraindications, side effects, and effectiveness and they must also meet speciﬁc guidelines for readability and size of print. Since 1962, Rx drugs have been advertised and marketed not only to physicians (DTPA), but also more directly to consumers (DTCA). Given these factors, and the fact that health care and pharmaceutical drugs constitute a very signiﬁcant component of our economy, the investigation of advertising effects on market share is important and worthy of our attention. The rest of this paper is organized as follows. First, we provide a brief overview of the relevant literature. Next we describe the data and the statistical models. We follow this with the presentation of the empirical results and a discussion of the results. We close with a discussion of managerial implication and identiﬁcation of future research possibilities. Brief overview of relevant literature The effect of direct advertising to consumers on market share is well-documented in marketing research. Most of the earlier research in marketing (Dorfman and Steiner, 1954; Corkindale and Newall, 1978; Simon and Arndt, 1980; Ghosh et al., 1984;
- 3. Kalyanaram and Wittink, 1994; Bronnenberg, 1998; Vakratsas and Ambler, 1999; Hanssens et al., 2001; Vakratsas et al., 2004) has generally concerned itself with frequently purchased, mature product categories, where the competitive environment is stable and, advertising budgets are set. However, many recent studies have examined the effects of advertising in the pharmaceutical industry. Berndt et al. (1995) studied the elasticity of marketing instruments in anti-ulcer prescription drugs category, and found that the sales elasticity was the greatest for detailing stocks and smallest for DTCA. They also found that the sum of the elasticities of direct marketing efforts at the category level was about 0.76, suggesting decreasing returns to scale to overall advertising at the product category level. Wosinska (2001) and Ling et al. (2002) examined the effect of marketing efforts directly to the end-consumers (DTCA) using data after the FDA’s 1997 additional clariﬁcation of DTCA guidelines. Wosinska’s (2001) study showed that DTCA efforts positively impacted total therapeutic class sales, but only impacted an individual brand positively if that brand had a preferred status on the third party payer’s formulary. Ling et al. (2002) found that DTC marketing efforts of OTC brands had no spillover to the same brand in the Rx market. Within the Rx market, own-brand physician-oriented detailing and medical journal advertising efforts had positive and long-lived impacts on own Rx market share, while DTC marketing of the Rx brand had no signiﬁcant impact on own Rx market share. Both these studies show that the effect of DTCA is limited and it is much smaller in magnitude than the effect of DTPA (which includes detailing). Using patient-level panel data, Bowman et al. (2004) report that DTCA had positive effect on some segments of consumers, but the effect was negative on other segments. Evidently, there is heterogeneity in the DTCA effect. In an effort to gain insights into better micro-targeting of the detailing, Narayanan and Manchanda (2004) modeled learning by physicians. The model was calibrated on data from erectile dysfunction drugs category. Ching (2005) employing aggregate data on sales, prices and marketing expenditures for ace inhibitors, addressed the question of why the detailing for a drug increases as the demand for the drug decreases. Narayanan et al. (2005) using data for prescription antihistamines, examined the temporal differences in the role of detailing in the initial launch phase and later in the life cycle of a drug. The results suggest that ﬁrms should follow heavier pattern communication at the introduction phase followed by lower levels in subsequent phases, and that only detailing effect is positive and signiﬁcant. In a 2005 study, Wosinka reports that while the advertising effects are statistically signiﬁcant, the returns on advertising are very modest (cents on a dollar) – a result similar to the ﬁndings by Berndt et al. (1995), Wosinska (2001) and Ling et al. (2002). In sum, the empirical results on DTCA effect in pharmaceutical industry are mixed. However, there are two reasonable empirical observations that empirical results support: (1) the effect of DTCA, when found to be signiﬁcant, is relatively small and generally much lower in magnitude than the effects of detailing and DTPA; and (2) there is heterogeneity among the consumers in the effect of DTCA. Endogenous modeling of the effect of DTCA 139
- 4. IJPHM 3,2 140 Finally, many researchers (Dave and Saffer, 2009; Chintagunta et al., 2006; Cotterill et al., 2000; Villas-Boas and Winer, 1999; Erickson, 1992; Pindyck and Rubinfeld, 1991) have recognized the issue of endogeneity in the choices by ﬁrms, and particularly in the domain of advertising decisions. Quite often ﬁrms tend to set the advertising budget for a brand as a percentage of sales and/or proﬁts for that brand. That is, the choice of advertising budget is affected by the level of sales, and this makes the advertising decision endogenous. Incorporation of endogeneity is important for both substantial and methodological reasons. Accordingly, we model advertising an endogenous decision. Data We examine monthly data from January 1998 to December 1999 for three therapeutic classes of prescription drugs. The prescription (Rx) drug categories are: recent anti-depressants (SSRIs plus serotonin/norepinephrine reuptake inhibitors), proton pump inhibitors (PPI), and antihistamines drugs. These drugs are fairly general in application: they treat a large variety of ailments, are indicated for different patient populations and are prescribed by a number of different clinical specialties. Data were collected on all of the drugs in each of these three classes. The anti-depressants category consists of six brands. The brands and their FDA approval dates are Celexa (1998), Serzone (1994), Effexor XR (1993), Paxil (1992), Zoloft (1991) and Prozac (1987). The PPI category consists of three brands. The brands and their FDA approval dates are Aciphex (1999), Prevacid (1995) and Prilosec (1989). The antihistamines category consists of ﬁve brands. The brands and their FDA approval dates are Astelin (November 1996), Allegra (July 1996), Zyrtec (1995), Semprex-D (1994) and Claritin (1993). Monthly price, quantity data and DTPA for prescription (Rx) drugs were obtained from a health care consulting ﬁrm, IMS Health. Market shares were constructed from product-level data on sales for the drugs in each of the three classes. Market shares were weighted by the corresponding prices. There are at least two major components to DTPA. The ﬁrst component is detailing to the physicians. This data are produced from the records of a panel of physicians, and estimates of cost per detailing visit by the sales representative. The second component is targeted advertising to physicians. This is measured through an audit of medical journals on a monthly basis. So DTPA is aggregation of detailing expenditures and targeted medical journal advertising expenditures. The advertising and promotion directed to DTCA was obtained from Leading National Advertisers (LNA)/Media Watch Multi-Media Service is published on a quarterly basis by Competitive Media Reporting. This service reports Rx brand advertising expenditure estimates in ten major media: consumer magazines, Sunday magazines, newspapers, outdoor, network television, spot television, syndicated television, cable television, network radio, and national spot radio. The LNA/Media Watch Multi-Media Service includes only brands of companies spending a total of $25,000 or more year-to-date in the ten media measured. We gathered the quarterly data, and transformed the quarterly data into monthly data simply by apportioning the total expenditures equally to each of the three months in the quarter. Average cost of consumption of the drug data were obtained from the reports of the health care consulting ﬁrm, Cowen and Company, LLC (2007) report.
- 5. The sources of data are Cowen and Company, LLC (2007) report, IMS Health, the individual company reports, FDA Orange Book, the Red Book (2007) and the US Patents and Trademark Ofﬁce web site. Empirical models and calibration We model market share in each period as a function of DTCA, price, the intensity of competition as represented by the number of competitive brands, and DTPA. Research has shown that the intensity of competition in a product category impacts the level of market share. The extant empirical research (Chintagunta et al., 2006; Cotterill et al., 2000; Villas-Boas and Winer, 1999; Erickson, 1992) has shown that ﬁrms often make endogenous decisions such as decisions related to advertising and promotion. Research suggests that ﬁrms often make decisions about advertising and other marketing instruments based on their revenues or sales or market share (Rust, 1986; Leckenby and Ju, 1990; Lilien and Rangaswamy, 2004). That is, the ﬁrms make their future advertising allocations based on their current level of resources measured in terms of sales or market share. In fact, some ﬁrms follow a very simplistic rule: the next year’s advertising budgets for a brand is set as a percentage of sales or market share of the brand for this year. Thus, the advertising decision becomes a endogenous decision by the ﬁrm. However, there is no deﬁnitive theoretical estimate of the number of time periods by which the advertising decision lags the basis (such as market share) on which the ﬁrm makes the advertising decision. The estimate of the number of time periods (lags between market share and advertising decision) is empirically estimated. The estimate of the optimal lags depends on the product category. As recommended by Kakwani and Sowey (1996), we employ the goodness-of-ﬁt measure to determine the best two-stage least squares (2SLS) model – the only parameter subjected to sensitivity analyses being the number of advertising decision lags. The goodness-of-ﬁt measure is calculated as the square of the correlation coefﬁcient between the ﬁtted and raw values of the dependent variable. Accordingly, we model DTCA as a function of its market share (with the optimal number of lags, n, to be determined empirically) and the average cost per consumption usage. Our main interest of study is the market share model – we are interested in examining the effect of direct advertising to consumers in a regulated environment. However, it is necessary to model the advertising endogenously to measure its effect correctly (unbiased estimate), and that is main purpose of the advertising model/equation. The formal equations/models are as follows: LogðS it Þ ¼ l þ ðaÞlogðDTCAit Þ þ ðbÞlogðP it Þ þ ðdÞlogðN t Þ ð1Þ þ ðuÞlogðDTPAit Þ þ ðLi ÞðBi Þ LogðDTCAit Þ ¼ s þ ðmÞlogðS it2n Þ þ ðvÞlogðACit Þ ð2Þ where, Sit represents the market share of the ith entrant as a ratio of the market share of ﬁrst entrant in the category at time t; DTCAit represents the advertising effort directly to the consumers of the ith entrant as a ratio of the advertising effort (DTC) of ﬁrst Endogenous modeling of the effect of DTCA 141
- 6. IJPHM 3,2 142 entrant in the category at time t; Pit represents the price of the ith entrant as a ratio of the price of ﬁrst entrant in the category at time t; Bi represents brand-speciﬁc constants (i ¼ 1, 10); Nt represents the number of competitors in the category at time t; DTCPit represents the advertising effort directly to the physicians of the ith entrant as a ratio of the advertising effort (DTP) of ﬁrst entrant in the category at time t; ACit represents the average cost of use of the drug per consumption of the ith entrant as a ratio of the average cost of use of the ﬁrst entrant in the category. The multiplicative form of the models allows for nonlinear response and interaction effects between the variables. We use ratio data for the models for many reasons. Since we intend to estimate equations (1) and (2) with time-series and cross-sectional data, ratios allow reasonable comparisons across categories with different numbers of brands. A second reason is that the ratios are an appropriate way of eliminating cross-category differences in marketing instruments, e.g. some categories have higher prices or promotional or advertisement expenditures and others have lower levels. Equations (1) and (2) must be estimated simultaneously. Our model postulates that market share and DTCA are interdependent and endogenously determined. 2SLS is widely employed to estimate simultaneous models, and obtain unbiased and efﬁcient estimates of the parameters/coefﬁcients. Consistent with this, we employ 2SLS to estimate the parameters of the model shown by equations (1) and (2). Equations (1) and (2) are jointly estimated. We have included brand-speciﬁc constants to account for missing variables and heterogeneity across the brands. Since there are three product categories and 14 brands across the three categories, we require ten brand-speciﬁc constants. One brand in each product category is the ﬁrst-brand, and that leaves us with 11 brands. However, to avoid singularity we have to drop one brand-speciﬁc constant. Postulates Consistent with earlier research ﬁndings, we postulate and expect the following empirical results. With respect to market share, we expect the following results: . DTCA will have a positive and statistically signiﬁcant impact. . The coefﬁcient of price will be negative and statistically signiﬁcant. . As the number of competitors increase, the level of market share would decrease because of the competitive pressures. With respect to DTCA, we expect that: . Lagged market share will have a statistically signiﬁcant and positive impact on advertising expenditure, i.e. higher market share will lead to higher advertising expenditure and this is consistent with general prior advertising results. . The coefﬁcient of the average consumption cost per usage variable will be positive and statistically signiﬁcant. Greater cost would require greater advertising investment by the ﬁrm to educate and persuade the consumers of the increased beneﬁts of the drug.
- 7. Empirical results and discussions The statistical results of estimating the market share and advertising equations jointly are shown in Tables I and II. Table I and Table II shows the results for the market share and advertising models. To determine the optimal length of lags for the advertising decision, we performed sensitivity analyses. We estimated the models with various lags for the advertising decision. We used the goodness-of-ﬁt measure to determine the optimality. The empirical results of the sensitivity analyses of the number of lags are reported in Table III. The values of goodness-of-ﬁt measures are 0.41 for one lag, 0.51 for two lags, 0.61 for three lags, 0.67 for four lags, and 0.59 for ﬁve lags. Thus, the data suggest that the four time-periods lag is the most optimal advertising decision lag for this product category and data set. So, it appears that the ﬁrms that are represented in this data set make advertising decisions for the next year ahead based on the resources available this year. Variable Parameter (coefﬁcient) Value þ 0.21 2 0.61 2 0.07 þ 0.62 þ 2.2 þ 1.9 2 1.1 2 0.95 þ 3.3 þ 2.4 2 6.7 þ 0.42 þ 3.2 2 3.3 þ 2.6 2 3.9 þ 2.1 þ 4.2 þ 3.9 þ 0.95 2 3.7 2 7.9 þ 1.4 þ 1.8 2 4.1 þ 2.1 þ 1.3 2 2.9 Effect of direct-to-consumers advertising (DTCA) Price effect (P) Effect of number of competitors (N) Effect of direct to physicians advertising (DTPA) Brand-speciﬁc constant 1 Brand-speciﬁc constant 2 Brand-speciﬁc constant 3 Brand-speciﬁc constant 4 Brand-speciﬁc constant 5 Brand-speciﬁc constant 6 Brand-speciﬁc constant 7 Brand-speciﬁc constant 8 Brand-speciﬁc constant 9 Brand-speciﬁc constant 10 Variable Lagged market share (Sit2 3) Effect of average consumption cost (AC) Number of lags One Two Three Four Five 143 t-statistic a b d u L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 Endogenous modeling of the effect of DTCA Parameter (coefﬁcient) Value t-statistic m v þ0.11 þ0.06 þ 5.2 þ 1.0 Table I. Market share equation Table II. Direct advertising equation Goodness-of-ﬁt 0.41 0.51 0.61 0.67 0.59 Note: For the 2SLS, goodness-of-ﬁt is calculated as the square of the correlation coefﬁcient between the ﬁtted and raw values of the dependent variable Table III. Sensitivity analyses estimation of the optimal lags for advertising decision
- 8. IJPHM 3,2 144 Since the optimal lag time period for advertising decision is estimated to be four time periods, we have reported the results of the complete model for four lags (for advertising decision) in Tables I and II. As postulated, the a parameter – the coefﬁcient of DTC advertising – is positive and signiﬁcant at the 1 percent level. So a greater level of DTCA increases the market share for the brand. The estimate of the magnitude of DTCA effect is 0.21. This result provides empirical support to the proposition that DTCA has the effect of the consumers seeking that advertised brand of drug. This ﬁnding is consistent with the argument advanced by insurers. The other parameters also provide interesting insights, and are consistent with our expectations. All the relevant estimates are signiﬁcant at 5 or 10 percent level. The price estimate (b) is negative and signiﬁcant (2 0.61). Given that all the product categories of study are prescription drugs, the price estimate appears to be reasonable. Since the prescription drugs can be bought and consumed only at the recommendations of the physicians, and since we are talking about a product category (drugs) where the choices are limited, if any (such as alternative therapies) available at all, it is not surprising that the price effect is less than one. The estimate of the coefﬁcient for the number of competitors is 2 0.07 and it is statistically signiﬁcant. As expected, greater level of competition reduces the ability to obtain higher market share. However, the magnitude (0.07) is relatively smaller because we are dealing with product categories which require a physician’s prescription. When there is uncertainty about a product’s performance, as it is with respect to the efﬁcacy of the drug, an expert’s (in this case a physician’s) recommendation is highly regarded. So this is not like other competitive product categories. The coefﬁcient of DTPA is estimated to be þ 0.62 and statistically signiﬁcant. It is thus clear that DTCA which includes detailing and targeting advertising has the biggest impact on market share. The effect of DTCA is substantially smaller in magnitude than the effect of DTCA. This is consistent with the other research ﬁndings. In the advertising equation, the coefﬁcient of lagged market share is estimated to be þ 0.11 and it is signiﬁcant suggesting, as postulated, that higher market share leads a ﬁrm to spend higher level on advertising. The reason for this is simple: ﬁrms set advertising budget as a function of its sales and market share. This is not a correct strategic approach for the ﬁrm as it is backward looking and not anticipatory. However, many ﬁrms make advertising decisions routinely in this manner, and this obviously makes the decision endogenous. Hence, we needed to model the market share and advertising expenditure simultaneously. Finally, as the cost of consumption per usage increases the ﬁrm has to expend more on advertising to educate the consumers about the beneﬁts. Advertising has to persuade consumers that the additional cost is worth the beneﬁt and this obviously requires a more substantial advertising budget commitment. This hypothesis is supported by the empirical results. The estimate of coefﬁcient of the cost consumption variable is 0.06, and it is statistically signiﬁcant. Managerial implications The results of this study provide empirical support to the proposition advanced by medical insurers and providers that DTCA has the effect of the consumers seeking that
- 9. advertised brand of drug. If the primary demand for the drug is assumed to be relatively stable, the empirical result suggests brand-switching behavior on part of the consumers. Considering that the three drugs in our empirical study are prescription drugs, and that they are applied for important ailments (depression, acid reﬂux, and allergies), it is reasonable to assume that the primary demand for drugs in these three categories were not increasing in 1998-1999. Therefore, our empirical result is strongly indicative of brand switching providing some support for the proposition argued by the medical insurers and providers. However, these results neither support nor refute the position argued by pharmaceutical ﬁrms. Further research is required to investigate all aspects of the implications of DTCA. While the effect of DTCA (þ 0.21) is signiﬁcant and substantial, this effect is less than the effects of price (2 0.61) and direct advertising to physicians (þ 0.62). Therefore, price and DTPA are better instruments for ﬁrms to changes market share assuming that the costs of implementation of all the instruments (DTCA, DTPA, and price discounts) are similar. Using the market share equation estimates, a ﬁrm can study the effects of each one of the instruments such as DTCA, DTPA and price or a combination of these instruments, and make managerial decisions regarding their effectiveness. One of the other interesting results in this study with practical implications is the effect of the cost of the drug on DTCA. As anticipated, the results show that a ﬁrm needs to advertise more aggressively to the consumers as the cost of unit dosage of the drug increases. This study quantiﬁes (þ 0.06) the effect of cost. Consistent with the fact that the consumers are likely to resist a higher cost drug, this study conﬁrms that the ﬁrm will have to invest more resources in DTCA to persuade the consumers of the better quality and/or efﬁcacy of the drug. Given the quantiﬁcation of the effect, ﬁrms can estimate the additional required investment in DTCA to offset the increased cost in drug. Thus, the empirical results offer several managerial insights. Managers can also compute the effects of various instruments. It is important to point out the caveat that the estimated magnitudes are useful for understanding the effects but these magnitudes must not be treated with certitude until they are corroborated with many other empirical studies. Future research Given the enormous implications for innovation and public policy, this topic of effect of DTCA in the pharmaceutical drugs deserves serious attention from scholars and practitioners. Our empirical results are encouraging, and complement and expand the extant knowledge base of empirical results and managerial insights. However, we identify two caveats and important directions for future research. First, the number of categories used in this study is somewhat limited. We have three categories in the prescription (Rx) drugs market. The concern about relatively smaller number of product categories is substantially alleviated by the robust number of brands (14 of them), and adequate time-series data (24 monthly data). However, an evident direction for future research is to replicate this study in larger number of product categories, and examine if the empirical results presented in this paper Endogenous modeling of the effect of DTCA 145
- 10. IJPHM 3,2 146 are replicated. Second, the future study must incorporate other important covariates. For example, the incorporation of variables representing patents and generics would be useful addition to the model. However, inclusion of brand-speciﬁc constants should mitigate any biases in the estimation of the interested parameters/coefﬁcients. There are other evident fruitful directions for future research. One of them is more sophisticated estimation methodologies. We can, for example, estimate the equations using varying-parameters approach. In this methodology, the parameter estimates are dynamically and more ﬂexibly estimated and therefore, the estimates would be theoretically superior. Another estimation methodology that could be productively employed is incorporation of heterogeneity in the parameter estimates, and, of course, this is a powerful approach that would produce unbiased estimates. A third approach would be to adopt a Bayesian methodology where the estimates are updated based on the new information about data. While it is likely that more sophisticated estimation methodologies would provide slightly better estimates, many empirical studies have shown the OLS and 2SLS estimates to be quite robust. Therefore, we are very conﬁdent of our empirical results. While our results demonstrate empirical support for the proposition argued by the medical providers and insurers, there is more interesting work to be yet done. There are at least two other interesting research questions that have practical implications. First, is there an effect of DTCA on quantity demand for the product category and brands? If there is such an effect then that establishes some support for proposition advanced by the pharmaceutical ﬁrms. Second, is there a simultaneous effect of DTCA on both quantity demand and share? If there is an effect on both quantity demand and market share then that establishes some support for both the propositions. References Berndt, E.R., Bui, L., Reiley, D.R. and Urban, G.L. (1995), “Information, marketing and pricing in the US anti-ulcer drug market”, American Economic Review, Vol. 85 No. 2, pp. 100-5. Bowman, D., Heilman, C. and Seetharaman, P.B. (2004), “Determinants of product-use compliance behavior”, Journal of Marketing Research, Vol. 41 No. 3, pp. 324-38. Bronnenberg, B.J. (1998), “Advertising frequency decisions in a discrete Markov process under a budget constraint”, Journal of Marketing Research, Vol. 35 No. 3, pp. 399-406. Ching, A. (2005), “The effects of detailing on prescribing decisions under quality uncertainty”, working paper, University of Toronto, Toronto. Chintagunta, P., Kadiyali, V. and Vilcassim, N. (2006), “Endogeneity and simultaneity in competitive pricing and advertising: a logit demand analysis”, Journal of Business, Vol. 79 No. 6, pp. 2761-87. Corkindale, D. and Newall, J. (1978), “Advertising thresholds and wearout”, European Journal of Marketing, Vol. 12 No. 5, pp. 327-78. Cotterill, R., Putsis, W. and Dhar, R. (2000), “Assessing the competitive interaction between private labels and national brands”, Journal of Business, Vol. 73 No. 1, pp. 109-37. Cowen and Company, LLC (2007), Report on Biotechnology: Industry Outlook, Cowen and Company, LLC, New York, NY. Dave, D. and Saffer, H. (2009), “Direct-to-consumer advertising and pharmaceutical prices”, paper presented at the Eastern Economic Association Conference, New York, NY.
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- 12. IJPHM 3,2 148 About the author G.K. Kalyanaram is a Professor, a Management Consultant and a Corporate Advisor. He got his PhD from Massachusetts Institute of Technology in 1989. He has published extensively in several professional journals including International Journal of Pharmaceutical and Healthcare Marketing, International Journal of Research in Marketing, Journal of Consumer Research, Journal of Marketing Research, Journal of Product Innovation Management, Marketing Science, Review of Industrial Organization, and Strategy and Business. His research has been recognized by the American Marketing Association, the American Marketing Science Association, and the INFORMS. He has been recognized by MIT with the Harold Lobdell Jr Award for his contributions to the institute and its alumni. G.K. Kalyanaram can be contacted at: kalyan@alum. mit.edu To purchase reprints of this article please e-mail: reprints@emeraldinsight.com Or visit our web site for further details: www.emeraldinsight.com/reprints

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