Gurumurthy Kalyanaram on Endogenous Modeling of DTCA in IJHPM. Dr. Gurumurthy Kalyanaramh as lectured in various universities including The London School of Economics, The University of Texas at Dallas (UTD), Jiang Xi University of Finance and Frankfurt School of Finance and Management.

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
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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.
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modeling of the
effect of DTCA
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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
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effect of DTCA
141

6. IJPHM
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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
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8. IJPHM
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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
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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.
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Endogenous
modeling of the
effect of DTCA
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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
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