Strategic Advertising: Evidence in the Pharmaceutical
Industry∗
Y UTIAN C HEN†
W EI TAN‡
May 24, 2011
Abstract
We investigate the change in advertising patterns of competing pharmaceutical firms in the short
period after a new entrant, Crestor, is petitioned to be removed from the Statin drug market. Using physician-level data, we find that Crestor’s main competitor, Lipitor, increases its advertising
intensity the most among physicians with whom Crestor already has a medium share of total prescriptions. Moreover, such change in advertising patterns does not occur with Crestor’s other
smaller opponents. These findings support the view that Lipitor strategically advertises in order
to maximize its long run profit, while raise doubt on the hypothesis that Lipitor’s advertising is a
normal competitive response to the bad news.
Keywords: Strategic advertising, Predation, Exit
JEL Classification: D43, L11, L13
∗
We would like to thank Steven Berry, Hugo Benitez-Silva, Sandro Brusco, Judy Chavalier, Iain Cockburn, Pradeep Dubey,
Sara Ellison, Avi Goldfarb, Ginger Jin, Fiona Morton, Marc Rysman, Matthew Shum, Konstantinos Serfes, Yair Tauman for helpful
discussions and comments. We also appreciate the suggestions from seminar participants at Yale University, Queens College, Drexel
University, 2007 International Industrial Organization Society meetings, 2007 Econometric Society Summer meetings and 2008 AEA
meetings.
†
‡
Department of Economics, California State University, Long Beach, CA 90840, USA. Email: ychen7@csulb.edu
Department of Economics, SUNY-Stony Brook, NY 11794, USA. Email: wtan@notes.cc.sunysb.edu
1
1
Introduction
The strategic usage of advertising has been thoroughly studied in theoretical works, where several rationales have been identified for strategic advertising to affect firms’ entry/exit decisions.
However, the empirical study on strategic advertising is just getting underway (Bagwell (2007)).
In this work, we analyze the change in advertising patterns of incumbent firms after a new entrant
was petitioned to be removed from the market. We find evidence supportive to the argument that
the dominant incumbent firm strategically advertised after this bad news to the new entrant, and
its intent is to induce the new entrant’s “withdrawal” thereby maximizing its long run profit.
Our investigation is based on a high-profile case in the Statin drug market. Crestor, a brand that
belongs to a therapeutic class of Statin drug, was approved by FDA in August 2003 and entered the
Statin drug market in late 2003. Shortly after its entry, several cases of severe side effects occurred
from the use of this brand. Alarmed by the potential risk, on March 4, 2004, Public Citizen, a very
influential consumer advocacy group, submitted a petition to the Food and Drug Administration
(FDA) to formally remove the brand from the market. This bad news was immediately picked
up by major news networks such as CBS. Interestingly, a different Statin brand, Baycol, was also
petitioned by Public Citizen in 2001. As a consequence of a similar bad news, Baycol withdrew
from the market.
In the period immediately following the bad news of Crestor, all Statin drugs increased their
physician detailing level.1 Among Crestor’s opponents, Lipitor is the dominant brand accounting
for more than 40 percent of the total sale of Statin drug, and is Crestor’s major rival. After the bad
news on Crestor, Lipitor increased its detailing visits by 12 percent.
The increase in the detailing visits by Crestor’s competitors gives rise to a number of research
questions. Since the bad news negatively affects the market demand of Crestor, is the post-news
detailing of Crestor’s competitors a normal competitive response to the news event, namely, a
1
Detailing is the major form of advertising in the pharmaceutical industry, referring to the visit of pharmaceutical
sales representatives at physicians’ offices. It is commonly viewed as a means of relationship building with a particular
physician. Throughout the work, the advertising of any Statin drug refers to the detailing visits to physicians’ offices
by the Statin drug. For more details on detailing, see Section 3.1.
2
short run profit maximizing behavior holding the market structure unchanged? Or, is there any
strategic consideration underlying their detailing visits such that they are exploiting the bad news
by maximizing their long run profits? In particular, since the bad news does not generate the
pressure for Crestor to fully pull out from the Statin drug market, Crestor’s competitors may
strategically advertise (through detailing) to induce Crestor to cease relationship building with
certain group of physicians, so that they can benefit in the long run when Crestor takes a less
aggressive stance in their competition. Such a strategic intent in essence is “predatory” if each
physician is treated as a “physician market” and Crestor’s ceasing of relationship building with a
physician is treated as the “exit” of Crestor from that physician market.2
If the above “predatory” incentive exists in the post-news advertising of Crestor’s competitors,
they may practice predation only with certain group of physicians. The intuition is articulated
as below. While the bad news negatively affects physicians’ willingness to prescribe Crestor, the
profitability for its competitors to predate varies across physicians. For physicians whose demand
of Crestor is high prior to the bad news, the post-news demand is likely to be large enough for
Crestor to stay in profit through relationship building, making predation impossible. On the other
hand, for physicians whose demand of Crestor is low prior to the bad news, the post-news demand
is likely to be so low that Crestor will naturally give up expensive relationship building, making
costly predation unnecessary. Therefore, the strongest incentive for Crestor’s competitors to prey
shall arise among physicians whose prior demand of Crestor lies in the medium range.
Since Lipitor is the dominant brand in the Statin market and also Crestor’s major rival, we
focus on Lipitor’s advertising behavior to detect strategic considerations in the post-news advertising. The data set we use is a physician-level panel data set covering the periods preceding and
following the bad news. For each physician, we observe the prescription level and the number of
sales representatives’ visits of each Statin brand. A novel feature of our work is that we treat each
2
It is expensive for a pharmaceutical firm to send out sales representatives to visit and thus build up relationship
with physicians (see Section 3.1). Once Crestor “exits” by giving up relationship-building with certain physicians, it
is reasonable to expect that Crestor faces high re-entry costs, especially in the period when there are skeptics among
physicians on its safety due to the bad news.
3
physician as a market and Crestor’s ceasing of relationship building (i.e., detailing) with a physician as its withdrawal from the physician market. The relatively large number of physician markets
allows us to investigate the advertising pattern of Lipitor among different physician markets.3
The method we use in the baseline empirical work includes a Difference in Difference study
and a parametric regression model. Both methods give us consistent findings. After the bad news,
Lipitor demonstrates a significantly larger increase in its advertising among physicians with whom
Crestor has a medium share of prescriptions, relative to the increase in its advertising among
physicians where Crestor’s share is either low or high. These results are robust under various
model specifications. Interestingly, we also find that such a pattern in Lipitor’s advertising is more
prominent among physicians who have relatively small number of total Statin drug prescriptions.
One possible explanation to the above empirical findings is based on a strategic view of Lipitor’s advertising. Namely, Lipitor’s detailing is “predatory” in the sense that it is aimed at inducing Crestor to give up relationship building with physicians where Crestor has a medium share
of prescriptions, so that Lipitor can maximize its long run profit. Chen and Tan (2011) offer a
theoretical justification to the “predatory” response of Lipitor to the bad news.4 They consider
each physician as a physician market and that Crestor exits from a physician market if it gives up
relationship building with the physician. By formalizing the post-news competition between Lipitor and Crestor into a signaling game, they show that the above empirical findings are consistent
with the theoretical prediction on Lipitor’s predation. Namely, among physicians where Crestor
takes a medium share of prescriptions prior to the bad news, Lipitor advertises with the intent of
inducing Crestor to exit those physician markets so that it can maximize its long run profit.
There can be other explanations to the above empirical results. One may suggest that the
change in Lipitor’s advertising patterns is simply a normal competitive response to the bad news.
That is, holding the structure of the Statin market as fixed, Lipitor increases its advertising to
maximize its post-news short run profit without taking into consideration how Crestor will respond
to its advertising. There exists major difference between the “strategic” view and the “normal
3
From now on, we use “market” or “physician market” to refer to the individual physician market and “Statin
market” to refer to the aggregate market of Statin brands.
4
Chen and Tan (2011) is available at http://www.csulb.edu/ ychen7/.
4
competitive” view. In the strategic view, Lipitor is sacrificing part of its short run profit in order
to maximize its long run profit when Crestor is induced into a softer competitor in the future;
whereas such a strategic consideration is missing in the normal competitive view.
To investigate which view is better supported by empirical evidences, we carry out more empirical tests. The first test examines Lipitor’s advertising in physician markets where other Statin
brands take medium market shares. Since the news affects only Crestor but not other Statin
brands, Lipitor’s advertising among physicians where other Statin brands take medium shares
should mainly capture a normal competitive behavior. We find that, Lipitor does not exhibit a similar upward distortion in its advertising with physicians where other Statin brands have a medium
share in prescriptions. While this finding to some extent favors the “strategic” view, one caveat is
that the absence of such an upward distortion in Lipitor’s advertising could be purely driven by the
fact that these brands are not affected by the bad news, rather than by a lack of predatory intention.
Therefore, we carry out a second test. We explore the change in advertising patterns of the second
and the third seller of Statin brands, Zocor and Pravachol, in physician markets where Crestor
take different market shares. We note that the market share of these two brands is much smaller
than that of Lipitor; besides, their patents are expiring soon at the time of the bad news. Zocor
and Pravachol’s patents expire in 2006, while the patent of Lipitor expires in 2010 (see Table 1).
Therefore, predatory incentive with these two brands, if there is any, should be much weaker than
that of Lipitor. On the other hand, these two brands still possess the competitive incentive to exploit the bad news and maximize the post-news short run profits. Therefore, advertising patterns of
Zocor and Pravachol mainly capture a normal competitive response to the bad news in the lack of
a predatory intention. We find that, their advertising patterns change from before to after the news
in a significantly different way compared to Lipitor. In particular, neither brand increases its advertising the most among physicians where Crestor has a medium share. To address the issue that
these differences may result from the fact that Zocor and Pravachol take a much smaller market
share relative to Lipitor, we also examine the advertising behavior of Zocor and Pravachol when
we only look at physician markets where Zocor/Pravachol has a relatively large share. Our results
indicate that similar findings persist with the reduced sample. Namely, neither of them increases
5
advertising the most among physicians where Crestor has a medium share.
The rest of the paper is organized as follows: Section 2 reviews the related literature. Section
3 introduces the background regarding the Statin drug market and the bad news event. Section 4
describes the data. After that, Section 5 provides the empirical strategy together with a Differencein-Difference study. Section 6 gives the empirical model and results. Section 7 compares two
possible explanations to the empirical results: the strategic view and the normal competitive view.
More empirical tests are given in Section 8 to see which view is better supported. Then Section 9
concludes.
2
Literature Review
Our work is closely related to empirical studies on predatory practice. To our knowledge, only
a few empirical evidences of predation exist in contrast to the extensive theoretical studies on
predation.5 Among the empirical works, Morton (1997) examines how the British shipping cartel
responds to the entry of new shipping lines. She finds that incumbent cartels are more likely to
start a price war after the entry of “weaker” firms, which supports the “long purse” theory of
predation. Genesove and Mullin (2006) study the price war following the entry into the American
sugar refining industry. By first comparing the price to the marginal cost and then comparing the
predicted competitive price-cost margin to the observed margins, they suggest that the price wars
following the entry are predatory. Complementing the existing empirical studies on predation, our
work detects predatory behavior by examining the link between predatory incentives and market
properties. With a relatively large sample size, we are able to test whether Lipitor’s advertising
5
Beginning in the 1950s, “Chicago School” economic analysis, assuming complete information, rightfully pointed
out the irrationality of monopolizing a market through predation (McGee (1958, 1980)). On the other hand, modern
strategic analysis have resurrected the logic of predatory practice. The key assumption is the absence of perfect information; under such conditions, predation becomes a rational strategy even for a predator without efficiency or financial
advantages over its prey. Many intrinsic incentives have been identified in favor of predation. To cite several of these
works, see Milgrom and Roberts (1982), Kreps and Wilson (1982), Fudenberg and Tirole (1986), Roberts (1986),
Saloner (1987), etc..
6
pattern across different markets is consistent with the prediction of predation.
Another line of related literature to our work is the empirical study of entry deterrence with
pricing/advertising strategy. Ellison and Ellison (2007) analyze the behavior of pharmaceutical
incumbents in the period prior to the expiration of their patents. They identify evidences of entry
deterrence by investigating whether the incumbent’s behavior is non-monotonic in the size of the
market. While large markets are so attractive to the entrant that deterring entry is never optimal,
small markets inherently defy entry thereby making entry-deterring investment unnecessary. Consequently, the strongest incentive of entry deterrence shall arise within markets of medium size.
In a manner closely related to Ellison and Ellison (2007), our work suggests that the strongest
incentive for Lipitor to predate shall arise within physician markets where the demand of Crestor
is in the medium range. More related works include Morton (2000), where she tests whether incumbents facing generic entry increase advertising in order to deter entry and finds no evidence
supportive of brand advertising as constituting an entry barrier; Goolsbee and Syverson (2008)
study how incumbent carriers of major airlines respond to the threat of the entry of Southwest
Airlines. They find evidence consistent with theories claiming that incumbents try to construct
longer-term loyalty before entry occurs.
Our work is also related to the large empirical literature on market entry and exit. The majority of empirical studies on entry and exit belong to the class of “equilibrium entry/exit models”
pioneered by the influential studies of Bresnahan and Reiss (1988) and Berry (1992). A common
feature of those studies is their assumption that the observed market structure is at equilibrium.
Thus even though the studies try to understand entry and exit, no actual entry or exit is observed
in the data. In addition, equilibrium entry/exit models often ignore how firm strategies – such as
price and advertising – affect entry and exit. Instead, our work focuses on the possibility of the
strategic usage of advertising to induce exit.
There is literature empirically testing strategic advertising to signal product quality. Using
U.S. automobile industry data, Thomas et al. (1998) find an upward distortion in advertising with
products whose prices are also upward distorted. This finding is consistent with signaling game
theory, which says that firms with higher quality would use advertising together with price to
7
signal the quality of unobserved attributes. Horstmann and MacDonald (2003) test if advertising
is a signal of product quality in the compact disc player market. They find that the advertising and
pricing patterns can be explained by signaling game involving persistent consumer uncertainty
and learning.
The theoretical literature offers several justifications for strategic advertising. One argument is
based on the “goodwill” effect of advertising. Schmalensee (1983), Fudenberg and Tirole (1984),
Doraszelski and Markovich (2007) show that when advertising generates “captive” consumers,
incumbents may either under-invest or over-invest in advertising to deter entry. A second rationale
is based on the “noise” effect of advertising. Hilke and Nelson (1984) argue that an incumbent
may overinvest in advertising to jam consumer’s message space, therefore inducing rival’s exit.
The third line of explanation is based on the “signaling” effect of advertising. Bagwell and Ramey
(1988, 1990), Linnermer (1998) show that an incumbent may distort its advertising to signal its
private information regarding cost/demand/quality, aimed at deterring entry.
3
Background
3.1
The Statin Drug Market
Statins are drugs that help lower cholesterol levels. With at least 12 million Americans taking
cholesterol-lowering drugs — most of them Statins — and experts recommending that another
23 million take them, Statin drugs are certainly very important to the pharmaceutical industry. In
many cases, Statin drugs are the number one seller of their company. With sales for 2004 reaching
US $10.8 billion and 5.2 billion respectively, the top two brands in the market, Lipitor and Zocor,
are not only the largest seller for their maker, Pfizer and Merck & Co, but also the largest two
selling drugs in the world.
Table 1 tabulates for the major Statin brands including chemical content, production method,
generic availability, makers, market share in 2004 and patent expiration date. Based on chemical content, there are six competing products in the market:6 Crestor (rosuvastatin), Lescol (flu6
If two drugs have the same chemical content, they should be considered the same product from a chemical point
8
vastatin), Lipitor (atorvastatin), Pravachol (pravastatin), Zocor (simvastatin) and Mevacor (lovastatin). Only lovastatin has a number of generic products. During our study period, although
generic brands are available, the market is dominated by the branded drug. The four main brands
in the market are Lipitor, Zocor, Pravachol and Crestor. Their makers are Pfizer, Merck, BristolMyers Squibb and Astra Zeneca respectively. Among them, Lipitor and Crestor are considered
closer substitutes as they use the same production method (Synthetic). Lipitor is the dominant
brand counting for 43 percent Statin market share. The second and third largest brand, Zocor and
Pravachol, count for 23 percent and 11 percent of the total sale, respectively. Among the major
brands, Zocor and Pravachol’s patents expire in 2006, while Lipitor’s patent expires in 2010 and
Crestor’s in 2012.
[Insert Table 1 Here]
Pharmaceutical companies mainly compete through advertising and marketing promotions.
Excepting direct to consumer advertising, the most commonly used marketing methods are pharmaceutical sales representatives’ visits to physicians’ offices, referred to as “detailing”. Detailing
is widely viewed as a means of building a relationship with a particular physician. During the office visit, a sales representative will show the physician various findings that support the use of the
relevant drug. The sales representative will also leave free samples for the physician to distribute
to patients. According to industry convention, each sales representative is only allowed to visit
the same physician once a month. However, if a firm wants to have more than one detailing per
month at a given physician’s office, it can employ several sales representatives, having each visit
the physician at a different time during the month.
3.2
News Event
The drug that suffered the bad news is Crestor. It first obtained FDA approval on August 12th, 2003
and entered the Statin market shortly afterward. After entering the Statin market, several severe
of view. Of course consumers may have different preference towards drugs of the same content. This difference may
be caused by various reasons such as brand image effect, advertising and different ways of packaging the product, etc.
9
cases of side effects occurred. Alarmed by the findings, a very influential consumer advocacy
group known as Public Citizen submitted on March 4th, 2004 a formal petition to FDA to have the
drug removed from the Statin market. According to its petition, “three patients in the United States
who were taking approved doses of rosuvastatin developed kidney failure or muscle damage. One
of those patients, a 39-year-old woman, died of kidney failure and rhabdomyolysis, or muscle
damage. Data obtained from the United States, the United Kingdom and Canada show seven cases
of rhabdomyolysis and nine case of kidney damage or failure occurred after FDA approval.” Major
news networks such as CBS immediately broadcasted the news about the petition from Public
Citizen. Crestor’s maker Astra Zeneca wouldn’t comment on the deaths or any other serious side
effects, except to say that “the safety profile is totally comparable” to what pre-marketing studies
had predicted.
Interestingly, this was not the first time that Public Citizen issued a petition for the removal
of a Statin drug. Baycol, another Statin drug, withdrew from the Statin market in the fall of 2001
following a petition by Public Citizen to FDA. The side effects of Baycol were very similar to
those of Crestor in that they were both likely to cause kidney damage. Not surprisingly, Public
Citizen emphasized the similarity of the two drugs and noted the withdrawal of Baycol following
their petition to FDA.
The news breakout is clearly not anticipated by a regular physician or a patient. Although it
is possible that before the news event, physicians may have heard about the incidence of severe
side effects from other sources such as sales representative visits, medical journals and professional conferences, etc., the formal petition of Public Citizen to FDA and subsequent mass media
coverage still brings additional information to physicians. Moreover, it is very likely that this is
the first time that a patient will receive any news about Crestor’s side effect. As many studies
have argued that patient also plays an important role in medical treatment decision, patient’s reaction may influence physicians’ prescription decisions. Therefore, the news event provides us a
unique opportunity, i.e., a “natural experiment”, to study the changes in advertising strategies of
pharmaceutical firms after the news event.
Although the news event imposes negative impact on the demand of Crestor, it is not severe
10
enough to force Crestor to fully pull out from the Statin market. While Crestor remains active
in detailing after the news, its competitors, especially Lipitor, may employ predatory strategy to
convince Crestor that continuing to detail certain physicians is no longer profitable. If this is the
case, Lipitor’s incentive to prey shall vary in physicians’ prior demand of Crestor. In the following
part, we first describe our data; then we investigate the change in Lipitor’s advertising before and
after the news among groups of physicians where prior demand of Crestor differs.
4
Data
The data we use for the empirical analysis is from a category of prescription drugs used to treat
high levels of blood cholesterol; such drug is commonly referred to as Statin drug. The data are at
the level of physician and are based on a panel of representative physicians from the United States.
These data are obtained from a pharmaceutical consulting firm, which also provided a similar
dataset to a marketing study by Narayanan and Manchanda (2005). For each physician, we observe
a sample of prescriptions between January 1st, 2004 and December 31st, 2004. In addition, we
also have a record of all the detailing visits made by pharmaceutical sales representatives during
the same period. The dataset is unique in that we observe both the prescription level and the
detailing frequencies of all Statin brands at the physician level.7
Since each sales representative can visit a physician’s office only once per month according
to the industry convention, it is likely that pharmaceutical firms make their advertising decision
on a monthly basis. Therefore, we create a monthly physician panel data. For each physician,
we calculate the total number of sales representatives’ visits and total prescriptions for all Statin
brands in each month. Since the news event happens on March 4th, 2004, which is the 9th week
in our dataset, we decide to exclude week 9 from our study to give firms enough time to adjust to
the news event. Our study focuses on changes in detailing patterns right after the news, so we use
a short time window and exclude observations after week 13, which is five weeks after the news
7
A limitation of this data set is that we do not observe the minutes of each detailing visit. Therefore, we can not
tell whether actual physician contact occurs or not during a detailing visit. However, we think that number of detailing
visits still serve as a good proxy for detailing efforts.
11
event. We make some adjustment in our definition of a month. We divide the time before and after
the news into three periods of equal length: week 1-4 (January), week 5-8 (February) and week
10-13 (March). An advantage of constructing the time period this way is that we make sure our
comparisons are based on the same length of periods.
An important part of our study requires us to create measures of monthly market share. During
our sample period, Crestor’s share of total prescription is around 10 percent. However, some
physicians in our data set have a very small number of prescriptions during each month. We are
concerned that including those physicians will make our measure of market share very noisy and
increase the risk of misclassifying a physician’s market share level. For example, if we observe
only one prescription of Statin drug by a physician in a given month and that prescription happens
to be Crestor, we will infer that Crestor has a 100 percent market share in that physician market.
Clearly, if we classify this physician as having high Crestor market share, we are not as confident
as doing so for another physician who prescribes Crestor 10 times out of 10 prescriptions. In order
to have a reasonably large prescription levels to calculate market share, we restrict our sample to
physicians with 10 or more prescriptions in both week 1-4 (January) and week 5-8 (February).
This restricts our dataset to 308 physicians for whom we observe monthly detailing as well as
prescription levels in all three months.
Table 2 summarizes monthly prescriptions and detailing intensities for our sample physicians.
The statistics of prescriptions and market shares are based on the data in January and February;
and the statistics of number of detailing visits are based on the data in February and March. That
means for each physician, Table 2 summarizes two monthly prescriptions (January and February)
and two number of detailing visits (February and March) of each Statin brand. Therefore, the
sample size is 616. The average number of monthly total prescriptions is 20.54 with a maximum
of 75. Crestor’s average physician market share is 9 percent. Lipitor has the largest physician
market share at 43 percent, followed by Zocor at 24 percent and Pravachol at 13 percent. Table
2 also shows the distribution of market shares for each major Statin brand. Among physicians
with at least one prescription of Crestor, the 25th quantile, median and 75th quantile of Crestor’s
market share are 0.07, 0.11 and 0.20 respectively. It is worth mentioning that the distribution of
12
Pravachol’s market share is the closest to that of Crestor.
We do not observe physicians’ personal characteristics such as their education or type of practice. Nevertheless, we report in Table 2 measures of patients’ mix at physicians’ offices. These
measures include the (physician market) share of new patients, the share of older patients (age
65+), the share of Asian patients, the share of female patients and the share of severe-symptom
patients. On average, new patients, patients aged 65 or above, Asian patients, female patients and
patients with severe symptoms count for 10 percent, 47 percent , 2 percent , 47 percent and 6
percent, respectively.
Table 2 also summarizes the monthly number of detailing visits by each major Statin brand.
In February and March, the average number of detailing visits by Lipitor, Zocor, Pravachol and
Crestor are 0.83, 0.89, 0.58 and 1.29 respectively. Crestor has the highest level of detailing intensity in part due to that it just entered the Statin market about four months ago. In addition, because
of the release of the bad news, Crestor may increase its advertising level to counteract the negative
effect of the bad news. It is clear that all brands exhibit considerable variations in detailing intensity across physicians. For example, the maximum number of Lipitor’s monthly detailing visits to
a physician is 6, while to some physicians there is no detailing visit by Lipitor at all.
[Insert Table 2 Here]
5
Empirical Strategy
The objective of our work is to examine how Crestor’s competitors change their advertising across
different physician markets after the release of the bad news to Crestor. In particular, we investigate whether the change in their advertising is non-monotonic in the prior demand of Crestor, and
whether they increase advertising the most among markets where the prior demand of Crestor is
in the medium range. We focus on Lipitor’s response to the bad news as Lipitor is the dominant
brand in the Statin drug market and also the closest substitute for Crestor. Moreover, patents of
Zocor and Pravachol are expiring soon at the time of the bad news. Therefore, the incentive to
13
practice predatory advertising should be the strongest with Lipitor.8
As the bad news do not generate the pressure for Crestor to fully pull out from the Statin
market, the exiting decision of Crestor is more relevant at the physician level. Therefore, we
treat each physician as a market and define Crestor as having exited from a physician market if it
foregoes relationship building with that physician by ceasing to send sales representatives.9 We
use Crestor’s share in a physician’s total prescriptions before the bad news to capture the prior
demand of Crestor in that physician market.
We want to emphasize that we are examining the change in Lipitor’s advertising strategy
before and after the news event, rather than the actual outcome of such a strategy. The main
reason is that even if Lipitor’s post-news advertising is indeed predatory, it is very difficult to
know how long it takes to have any effect. In addition, the information flow is constant in the
Statin drug market. After the bad news event, there might be other additional information released
to the public. For example, we observe that FDA changed the warning on the label of Crestor three
months after Public Citizen’s petition. These additional information certainly will have effects on
Lipitor’s strategy, which we are not able to control for in our model. As such, we focus on the
short time window around the news event, so that the results are mostly driven by the bad news
rather than additional information released afterwards.
We focus on the changes in detailing strategy rather than price and other competition practice
such as direct to consumer advertising. The reason is that we look at the immediate response of
firms after the news event,10 whereas firms’ change in price and direct to consumer advertising
is likely to take longer time to occur. In addition, we explore how the changes in firms’ strategies differ across different types of physicians. In our data, only detailing activity varies across
physicians, while price and direct to consumer advertising are constant.
8
We will also examine the changes in Zocor and Pravachol’s advertising patterns in section 8 when we discuss
alternative explanations to our empirical results.
9
As long as Crestors competitors prefer its exiting (stopping detailing a physician), the intrinsic incentive of predation still holds under our definition of market exit.
10
We expect that the effect of the bad news is the strongest in the period immediately following the news event,
together with the incentives for Crestor’s competitors to use predatory strategies.
14
5.1
A Difference in Difference Study
The main idea of a Dif-in-Dif (Difference in Difference) Study is to compare the changes in the
outcome variable of the comparison group with that of the control group. In our baseline Difin-Dif estimation, the outcome variable is number of Lipitor detailing. Ideally, we would like to
look at the change in Lipitor’s number of detailing during a short time window around the date
of the news event (March 4th, 2004). However, we also need a large data set to provide a test
with appropriate power. As the news happens on March 4th (week 9), we compare the number of
detailing in February (week 5-8) with that in March (week 10-13). As described in Section 5, by
defining the time period this way, we guarantee that our comparisons are done over the exact same
length of periods.
In our baseline Dif-in-Dif estimation, the comparison group includes physicians whose prescription level of Crestor before the news falls in the medium range. For the control group, we use
two different specifications: physicians with low prescription level of Crestor and physicians with
high prescription level of Crestor. In this way, we control for the expected differences in detailing
due to other factors that are not associated with the news event such as the seasonal effect. It is
worth mentioning that our test objectives are different from the standard Dif-in-Dif estimation.
We are not trying to estimate the causal effect of news event on detailing level. Instead, we are
interested in testing if the detailing visits of Lipitor increases more in the comparison group than
in BOTH control groups.
We use the market share of Crestor in week 1-8 to categorize a physician. Unfortunately, there
is no guideline for us to determine the range of market share where predatory intent exists. We
decide to use the quantile of Crestor’s market share distribution to determine the ranges. Based
on week 1-8 prescription data, the 25th percentile of Crestor’s market share is at 0.05, and the
75th percentile is at 0.18. We choose to categorize a physician as low, medium, and high type if
the market share of Crestor is between [0-0.05), [0.05-0.18], and (0.18, 1] respectively. We try to
categorize more physicians in the medium type and avoid the potential problems of misclassifying
a medium type physician into either low or high type. The downside of having a large size of
medium type is that we may include low or high type physicians as medium type, which in turn
15
may weaken our test results. Clearly, the misclassification of market share ranges is a potential
problem. To check the robustness of our results, we also use alternative but similar definitions of
the range of Crestor market to categorize physicians. The results show that our qualitative results
continue to hold under alternative categorizations.11
The baseline Dif-in-Dif results are reported in Table 3. During the four weeks before the
news event (week 5-8, or February), the average number of Lipitor detailing among medium type
physicians is 0.76, while the average number of Lipitor detailing among physicians of low and
high types are 0.73 and 0.82 respectively. After the news event (week 10-13, or March), the
average number of Lipitor detailing increases to 0.92 among medium type physicians. Among the
low and high type physicians, the average number of Lipitor detailing increase to 0.81 and 0.89,
respectively. Based on the first difference results, the increase in number of Lipitor detailing for
the medium type physicians is 0.16, while the increase for low and high types physicians are 0.09
and 0.07 respectively. The Dif-in-Dif estimates (0.07 and 0.09) suggest that Lipitor’s detailing
increases the most among medium type physicians. However, due to the large standard error (0.18
and 0.24), we cannot conclude that the difference is statistically significant.
[Insert Table 3 Here]
A potential concern of the Dif-in-Dif study is the misclassification of Crestor’s medium market
share. We classify a physician to be the medium type if Crestor’s market share is between the 25th
and 75th quantile. In order to check whether the definition of the medium market share affects our
findings, we non-parametrically estimate the detailing level of Lipitor during the four weeks prior
to and after the news event as a function of the quantile of Crestor market share in week 1-8.12
11
For example, the qualitative part of the Dif-in-Dif results reported in Table 3 is unaltered if we use [0.04, 0.2] or
[0.06, 0.15] as the range of Crestor market share to categorize a physician as of the medium type.
12
The Nadaraya Watson kernel regression estimator is:
m(x)
=
n
∑
wni (x)yi
i=1
wni (x)
=
K(
n
xi − x ∑
xi − x
)/(
K(
))
h
h
i=1
where yi is the observed number of detailings, xi is the observed quantile of market share, and x is the value of quantile
16
Figure 1 plots the nonparametric estimates of Lipitor detailing and the quantile of Crestor
market share. The result shows that the change in Lipitor advertising is non-monotonic in Crestor’s
market share. Moreover, when Crestor’s market share is between the 40th and 70th quantile,
Lipitor increases the detailing level the most after the news event. For physicians where Crestor’s
market share is below 40th quantile or above 70th quantile, Lipitor’s advertising level increases
by a much smaller scale.
[Insert Figure 1 Here]
6
The Empirical Model and Results
Our baseline econometric model uses a regression based model to measure the impact of the bad
news on the detailing pattern of Lipitor. An advantage of a regression based model is that we can
control for additional market characteristics. In addition, as we have a panel data of physicians,
regression based model allows us to control for physician specific effect through random effect
model and fixed effect model.
We use the market share of Crestor in the previous month to categorize a physician. This
means that types of physicians observed in February (week 5-8) are determined according to the
market share of Crestor in January (week 1-4); types of physicians observed in March (week 1013) are determined according to the market share of Crestor in February (week 5-8). Similar as in
the Dif-in-Dif study, the quantile of Crestor’s market share is used to determine the high, medium
and low ranges of Crestor’s market share. Based on the prescription data in week 1-4 (January)
and week 5-8 (February), the 25th percentile of Crestor’s market share is at 0.07, the median is at
0.11, and the 75th percentile is at 0.2. We thus categorize a physician as low, medium and high
type if the market share of Crestor is between [0-0.07), [0.07-0.2], and (0.2, 1] respectively.13
of market share where we calculate the expected number of detailings.
13
The qualitative results of Table 4 is unaltered if we use alternative but similar definitions for physicians of low,
medium and high type. For example, we may categorize a physician as of the medium type if the market share of
Crestor is between [0.08, 0.18] or [0.06, 0.22].
17
To investigate how Lipitor changes its detailing pattern from before to after the bad news, we
run Poisson regression to estimate the basic specification:
yit = exp(β0 + β1 CM + β2 CH +
(γ0 + γ1 CM + γ2 CH) ∗ P ostevent + δXit + αi + ϵit ).
Here yit is the number of Lipitor detailing to physician i before (t = 1) and after (t = 2) the
news event. It is a non-negative integer. Similar to the above Dif-in-Dif estimation, we use four
weeks of window before and after the event. Thus t = 1 if the week is between the 5th week and
the 8th week, and t = 2 if the week is between the 10th week and the 13th week. CM and CH are
dummy variables indicative of Crestor’s previous market share as in the medium range and high
range respectively (recollect that we use week 1-4 market share to create previous market share for
t = 1 and week 5-8 market share to create previous market share for t = 2). Postevent is a dummy
variable equal to 1 for t = 2. Xit are market characteristics that may affect the detailing decisions.
These market characteristics include the market size (total number of prescriptions), the share of
new patients, the share of older patients (age 65+), the share of Asian patients, the share of female
patients, and the share of severe-symptom patients. These additional variables, all are based on the
data in the previous month (four weeks), help to control for other factors that might influence the
demand for Crestor. And αi is the physician specific effect in the random effect model and fixed
effect model. In all the regressions, we weight observations using the number of prescriptions of
the physician in week 1-8 in order that larger markets should have a greater impact on Lipitor’s
detailing response than smaller ones. Table 4 gives the results from the baseline specification
using random effect regression and fixed effect regression. The estimates we report include γ1 ,
which is the coefficient of CM ∗ P ostevent; and also γ0 and γ2 , the coefficients of P ostevent
and CH ∗ P ostevent respectively.
In Table 4, we first report the regression results in specification 1 and specification 2, where we
do not include Xit (market characteristics) in the regression. The estimated values of γ0 , γ1 , and
γ2 from the random effect models are 0.06, 0.07 and -0.13 respectively. All coefficient estimates
are significant. The estimates from the fixed effect model are 0.05, 0.05 and -0.11 respectively
18
and only the estimates of γ0 and γ2 are significant. First, the estimates from both specifications
are reasonably similar. They suggest that γ0 and γ1 are positive and γ2 is negative. Namely, after
the news event, Lipitor’s advertising level increases for physicians with low and medium Crestor
market share; while it decreases for physicians with high Crestor market share. In addition, the
magnitude of increase in Lipitor’s detailing is the highest with those physicians where Crestor’s
market share falls in the medium range.
Second, we report the regression results in specification 3 and specification 4, where we include Xit in the regression. The estimate of γ0 is 0.05 in random effect model and 0.07 in fixed
effect model. Estimates of γ1 are 0.08 and 0.07 respectively for random effect and fixed effect
model; while the estimates of γ2 are -0.05 and -0.12 respectively. The estimates from both specifications are similar. They show that Lipitor’s advertising level increases the most within physicians
with medium Crestor market share. This suggests that our findings are robust to additional control
variables.
[Insert Table 4 Here]
Here an interesting question arises. If Lipitor’s post-news advertising has predatory intent, will
such intent be more pronounced in markets relatively large (i.e., physicians with a large number
of Statin prescriptions), or in markets relatively small? On the one hand, Lipitor may benefit more
when Crestor exits a large market. Correspondingly, Lipitor may possess a stronger incentive
to prey in markets of relatively large size. On the other hand, advertising is more intensified
among physicians who prescribe a large number of Statins. As the marginal effect of advertising
diminishes, the predation of Lipitor becomes more costly. In addition, advertising may also be
less informative in large markets since those physicians tend to be more knowledgeable about
drug efficacy from their prescription experiences. Therefore, the incentive for Lipitor to prey can
be weaker in large markets as advertising becomes less effective.
To check either a larger market size strengthens or weakens the predatory intention, we separate the physician markets into two categories: large and small. The average number of monthly
Statin drug prescriptions in our sample is around 20. We thus define a physician market to be
19
“small” if its monthly total prescriptions both before and after the news event are less than 20.
Otherwise, we categorize it to be “large”. This categorization leaves us with 152 small markets
and 156 large markets.14 We then estimate our baseline model on the two samples separately and
the results are reported in Table 5.
[Insert Table 5 Here]
The top panel of Table 5 presents the baseline results for small physician markets. In specification 1 and specification 2 where we do not control for additional variables in the regression,
the estimated values of γ0 , γ1 and γ2 from the random effect models are 0.14, 0.15 and -0.28
respectively. All coefficient estimates are significant. The estimates from the fixed effect model
are 0.04, 0.47 and -0.15 respectively and only the estimates of γ1 and γ2 are significant. Secondly,
we report the regression results in specification 3 and specification 4, where we include additional
control variables in the regression. The estimate of γ0 is 0.13 in random effect model and 0.08
in fixed effect model. Estimates of γ1 are 0.18 and 0.5 respectively; and the estimates of γ2 are
-0.19 and 0.03 respectively. Both of the estimates of γ1 are significant. The estimates from all
four specifications are reasonably similar. They suggest that estimates of γ0 and γ1 are positive
and significant while estimates of γ2 are either negative or insignificant.
The change in Liptior’s advertising patterns in small markets shows a similar pattern compared
to the results using the full sample. Namely, after the news event, Lipitor’s advertising increases
the most among physicians where Crestor takes a medium market share. Moreover, such a nonmonotonic change in Lipitor’s advertising is more prominent when we only look at those small
markets, as the estimates of γ1 in the top panel of Table 5 are highly significant and larger than the
estimates in Table 4.
However, we could not find a similar pattern in the change of Lipitor’s advertising when we
only look at large markets, as shown by the bottom panel of Table 5. The estimates of γ1 in the two
random effect models (specification 1 and 3) are negative and insignificant at -0.01 and -0.02. In
the two fixed effect models (specification 2 and 4), the estimates of γ1 are negative and significant
14
Our empirical results are robust under alternative but similar definitions of large and small markets.
20
at -0.2 and -0.22 respectively. In large markets, Lipitor does not increase its advertising the most
among physicians where Crestor takes a medium market share.
The results in Table 5 show that the incentive for Lipitor to target markets where Crestor has
a medium market share is even stronger among smaller markets. While other interpretations can
exist, this finding is supportive to the argument that when a market becomes larger and is more
frequently detailed before the news, additional detailing after the news is less effective.
7
Strategic Response vs. Normal Competitive Response
The above empirical tests look at the changes in advertising patterns of Crestor’s major competitor,
Lipitor, from before to after the news event. The results suggest that Lipitor changes its advertising
in a non-monotonic way in the prior market share of Crestor. In addition, Lipitor increases its
advertising intensity the most among physicians with medium market share of Crestor.
There can exist different interpretations to the above empirical results. One possible explanation is based on a strategic view. According to this view, Lipitor’s post news advertising is not
aimed at maximizing its post-news short run profit. Instead, Lipitor advertises with a predatory
intent to induce Crestor to leave certain physician markets so that Lipitor can benefit in the long
run. The basic idea of predation is that a firm may sacrifice part of the profits that could be earned
under competitive circumstances in order to induce the exit of an opponent, therefore gaining additional profits in the future. Literature has provided various rationales for predatory behavior
based on game theoretical analysis. A common feature of these theoretical models is the existence
of asymmetric information.
Chen and Tan (2011) offer a justification to Lipitor’s predation based on an asymmetric information assumption between Lipitor and Crestor. They assume that when the bad news is suddenly released to the public, Lipitor possesses superior information regarding how severely the
bad news is going to impact physicians’ prescription behavior vis-a-vis Crestor. Such an assumption is based on the following reasons: first, Crestor is a new entrant at the time of the bad news
and it is Crestor’s first time experiencing such bad news. Therefore, there is likely uncertainty
21
in Crestor’s perception regarding how badly the news will affect each physician’s prescription of
Crestor. Second, Crestor’s competitors witness a similar bad news event on Baycol three years
ago.15 Moreover, they have interacted with physicians for a much longer period compared to
Crestor. Therefore, these competitors are likely to possess superior information regarding how
physicians will respond to the bad news of Crestor.
Chen and Tan (2011) constitute a two-period game to model the post-news advertising competition between Lipitor and Crestor among independent physician markets. Before the game starts,
the bad news of Crestor occurs. Nature chooses the effect of the bad news as either severe or mild,
and reveals the true effect only to Lipitor but not to Crestor. In period one, Lipitor and Crestor
choose their advertising intensities simultaneously, and each has its demand/profit realized. In
period two, Crestor makes its withdrawal decision based on its period-one demand. If Crestor
chooses to continue to operate, in stage three it infers the real effect of the bad news and compete
with Lipitor under symmetric information.
Chen and Tan (2011) show that in period one when Crestor remains active to infer information
about its future profitability of continuing to detail a physician, Lipitor may distort its advertising
to signal Crestor its private information regarding the effect of the bad news. The model in Chen
and Tan (2011) admits two types of equilibrium: separating equilibrium and pooling equilibrium.
In both of them, Lipitor upward distorts its advertising compared to its normal competitive advertising. By doing so, Lipitor further drives down the period-one demand of Crestor in order to either
signal Crestor an intrinsically unprofitable market situation (separating equilibrium) or to interfere
with Crestor’s reference procedure of the true effect of the bad news (pooling equilibrium).
Moreover, Chen and Tan (2011) show that such a predatory intent of Lipitor exists only in
physician markets where demand of Crestor prior to the news is neither too low nor too high. The
intuition is that while predation is unnecessary in physician markets with low demand of Crestor
15
Although Crestor’s producer Astra Zenecca must have observed the bad news event on Baycol, it is not operating at
the Statin market at the point of that bad news hence can only observe the event as an outsider. Therefore, its knowledge
regarding how physicians change their prescription behavior as a response to the bad news should be limited relative to
Crestor’s competitors.
22
prior to the news event, it is not possible in physician markets with high prior demand of Crestor.
Thus only in physician markets with medium prior demand of Crestor, Lipitor has the incentive to
employ predatory strategy.16
An alternative interpretation is offered by a normal competitive view to Lipitor’s non-monotonic
change in advertising from before to after the news. According to this view, Lipitor takes a normal
competitive response to the bad news by maximizing its post-news short run profit, and it targets
physicians with medium market share of Crestor while taking the current Statin market structure
as fixed. Recollect that as a profit maximizing firm, Lipitor will choose its advertising level in
each physician market such that the marginal benefit of advertising equals the marginal cost of
advertising. Given that the marginal cost of advertising is constant among physicians, Lipitor will
advertise more towards physicians where the marginal benefit of advertising is larger. Therefore,
if it is the case that the bad news enhances the marginal benefit of advertising more among physicians with medium market share of Crestor, the empirical findings can arise as a result of Lipitor’s
normal competitive response to the bad news. In the following section, we conduct more empirical
tests to investigate either the strategic view or the normal competitive view is better supported by
the empirical results.
8
Testing the Normal Competitive View
As pointed out above, in certain scenario Lipitor’s normal competitive response to the bad news
may lead to a non-monotonic change in its advertising to Crestor’s prior market share. In this
section, we use several additional tests to explore the possibility of this alternative explanation.
First, since the news only affects Crestor but not other Statin brands, Lipitor’s detailing distribution among physicians where other brands take different market shares should capture a normal
competitive behavior. If the non-monotonic change in Lipitor’s detailing is driven by Lipitor’s
predatory intent towards Crestor after the bad news, a similar pattern will not be found with Lipitor’s detailing towards physicians where other brands have medium market share.
16
The intuition of the results is very similar to that of Ellison and Ellison (2007), where the strategic use of advertising
to deter entry occurs only in markets with intermediate probability of entry.
23
Second, we expect that the two smaller brands, Zocor and Pravachol, with patents expiring
soon, have much weaker incentives (if existing at all) to engage in predatory practice compared
to Lipitor. On the other hand, they have the competitive incentive to maximize their short-run
profits in the post-news period. Therefore, their post-news advertising mainly captures the normal
competitive response to the bad news in the absence of a predatory intent. If the change in their
detailing patterns is significantly different from the change in Lipitor’s detailing patterns from
before to after the news, the evidence then favors the strategic view that a predatory intent underlies
Lipitor’s post-news advertising.
We follow the same approach as in Section 5 and 6 to do the additional tests. We begin with a
Difference-in-Difference study where we compare the difference in detailing intensity between the
comparison group and the control groups before and after the bad news. We then use a regression
based estimator which allows us to control for additional variables.
8.1
Change in Lipitor’s Advertising and Pravachol’s Market Share
As argued above, the predatory strategy of Lipitor can only be aimed at driving Crestor but not
other Statin brands to leave certain physician markets. Since Pravachol’s market share is the
closest to that of Crestor, we examine whether Lipitor increases its advertising intensity the most
among physicians where Pravachol has a medium market share.
We report in Table 6 the results of a Dif-in-Dif study when we use Pravachol’s market share
to construct the comparison group and the control groups, while keeping the number of Liptior
detailing the outcome variable. Namely, the comparison group includes physicians whose prescription level of Pravachol falls in the medium range. The control groups include physicians with
low prescription level of Pravachol and physicians with high prescription level of Pravachol.
After the news event, average detailing number of Lipitor to physicians with medium market
share of Pravachol increases from 0.82 to 0.90; while its detailing increases from 0.75 to 0.93 for
physicians with low market share of Pravachol and from 0.64 to 0.69 for physicians with high
market share of Pravachol. Thus Lipitor’s detailing intensity shows a much larger increase within
physicians where Pravachol has low market share, instead of within physicians where Pravachol
24
has medium market share. The Dif-in-Dif estimates are -0.10 and 0.03, with the first value negative.
[Insert Table 6 Here]
Figure 2 plots the nonparametric estimates of the number of Lipitor detailing and the quantile
of Pravachol market share. As the figure shows, Lipitor’s advertising increases the most after the
bad news among physicians with low Pravachol market share.
[Insert Figure 2 Here]
We conclude that the change in Lipitor’s detailing intensity shows a different pattern in Pravachol’s market share compared to its pattern in Crestor’s market share. This implies that a normal
competitive advertising of Lipitor may exhibit a different pattern compared to the pattern we observe among different Crestor market share. While this finding favors the strategic view to some
extent, there is one caveat. One may argue that such a difference is driven by the fact that the
bad news has no impact on Pravachol, rather than by Liptior’s predatory intent towards Crestor.
Therefore, we carry out a second test in the following part to further explore the possibility of
Lipitor’s normal competitive response to the bad news.
8.2
Change in Zocor/Pravachol’s Advertising and Crestor’s Market Share
The second and the third best sellers of Statin drug are Zocor and Pravachol. They take a much
smaller market share than Lipitor and their patents are expiring soon at the point of the bad news.
Thus their predatory intent towards Crestor, if exists at all, should be much weaker compared to
Lipitor. On the other hand, they have the competitive incentive to maximize their short-run profits
in the period following the bad news.
We run two additional Dif-in-Dif estimations by replacing the outcome variable in the baseline model (Table 3) with the number of Zocor detailing and the number of Pravachol detailing,
respectively. The comparison group and the control groups are constructed in the same way by
using Crestor’s market share. The results are included in Table 7. The mean value of the number
25
of Zocor detailing within physicians with medium, low and high market share of Crestor is 0.81,
0.72, and 0.79 before the news event; and it increases to 0.94, 0.98 and 1.07 after the news event.
The increases in number of Zocor detailing are higher among physicians where Crestor has low
and high market share (0.26 and 0.28). The Dif-in-Dif results are both negative (-0.13 and -0.15)
for Zocor. Moreover, changes in the number of Pravachol detailing differ from that of Lipitor as
well. The mean value of the number of Pravachol detailing within physicians with medium, low
and high market share of Crestor is 0.60, 0.49, and 0.43 before the news event; and it changes to
0.77, 0.47 and 0.66 after the news event. The Dif-in-Dif estimates are 0.2 and -0.06 respectively.
Changes in Pravachol’s detailing patterns is monotonic in Crestor market share.
[Insert Table 7 Here]
The above results show that Zocor and Pravachol respond to the bad news in a different way
compared to Lipitor. Nevertheless, one may argue that the uniqueness in the change of Liptior’s
advertising is driven by its unique properties (for example, due to its large market share) and its
response is still normal competitive. To investigate this point, we check Zocor and Pravachol’s
response to the bad news using a reduced sample, which includes only physician markets where
Zocor/Pravachol has relatively large market share. In the reduced sample for Zocor, we use physicians where Zocor’s market share in week 1-8 is above its median level (24 percent); and the
reduced sample for Pravachol includes only physicians where Pravachol’s market share in week
1-8 is above its median level (11 percent).
Table 8 presents the Dif-in-Dif results using the reduced samples. Compared to Table 7,
the results with the reduced samples are qualitatively the same. The Dif-in-Dif results are both
negative (-0.13 and -0.23) for Zocor, while for Pravachol, the Dif-in-Dif estimates are 0.21 and
-0.13 respectively. These results show that the changes in Zocor and Pravachol’s detailing patterns
differ from that of Lipitor even among markets where Zocor and Pravachol take a relatively large
market share.
[Insert Table 8 Here]
26
Figure 3 and Figure 4 present the results of nonparametric estimates of Zocor and Pravachol’s
detailing level. Compared to Lipitor, Zocor and Pravachol clearly have different changes in detailing patterns after the news event. The most significant increase in Zocor’s detailing happens to
physicians with high and low Crestor market share, while the most significant increase in Pravachol’s detailing happens to physicians with high Crestor market share.
[Insert Figure 3 and Figure 4 Here]
We then use a regression model to look at the changes in Zocor and Pravachol’s detailing. We
replace the dependent variable in the baseline model with the number of Zocor detailing and the
number of Pravachol detailing, respectively. Table 9 gives the results of the random effect model
and the fixed effect model. The top panel reports the results using the number of Zocor detailing
as the outcome variable. In specification 1 and 2, we do not include additional control variables.
According to the random effect model (specification 1), the estimated values of γ0 , γ1 and γ2
are 0.23, -0.14 and 0.05 respectively. The estimates of γ0 and γ1 are significant. According to the
fixed effect model (specification 2), the estimated values of γ0 , γ1 , and γ2 are 0.26, -0.14 and -0.09
respectively. All estimates are significant. In specification 3 and 4, we include additional control
variables. According to the random effect model (specification 3), the estimated values of γ0 , γ1
and γ2 are all significant at 0.21, -0.14 and 0.17 respectively. According to the fixed effect model
(specification 4), the estimated values of γ0 , γ1 and γ2 are 0.26, -0.01 and 0.08 respectively. Only
the estimate of γ0 is significant. Since the estimates of γ1 are either negative or insignificant across
all specifications, it is clear that Zocor does not increase its detailing the most among physicians
where Crestor has medium market share.
The bottom panel of Table 9 reports the results using the number of Pravachol detailing as
the outcome variable. In specification 1 and 2, we do not include additional control variables.
According to the random effect model (specification 1), the estimated values of γ0 , γ1 and γ2 are
0.07, 0.02 and 0.36 respectively. The estimates of γ0 and γ2 are significant. According to the
fixed effect model (specification 2), the estimated values of γ0 , γ1 and γ2 are 0.09, -0.09 and 0.41
respectively. All estimates are significant. In specification 3 and 4, we include additional control
27
variables. According to the random effect model (specification 3), the estimates of γ0 , γ1 and
γ2 are 0.05, -0.02 and 0.43 respectively. The estimates of γ0 and γ2 are significant. According
to the fixed effect model (specification 4), the estimates of γ0 , γ1 and γ2 are 0.1, -0.09 and 0.47
respectively. The estimates of γ0 and γ2 are significant. In all four specifications, the estimates
of γ1 are either negative or insignificant, while the estimates of γ2 are positive and significant.
This suggests that Pravachol increases advertising intensity more to physicians with high Crestor
market share.
[Insert Table 9 Here]
To summarize, both the Dif-in-Dif study and the regression results show that the changes in
the advertising patterns of the two smaller competitors of Crestor, Zocor and Pravachol, differ in
a significant way from that of Lipitor. We do not find a significant increase in Zocor/Pravachol’s
advertising intensity among physicians where Crestor takes a medium market share.
The additional results from the above empirical tests suggest that the upward-distortion in
Lipitor’s advertising among physicians with medium Crestor market share is unique. Lipitor does
not show a similar advertising pattern among physician markets where Pravachol takes a medium
market share; nor do Zocor and Pravachol exhibit similar patterns in their advertising among
physicians where Crestor takes a medium share. These results are consistent with the strategic
view of Lipitor’s response to the bad news described in Section 7, and can hardly be justified by
the hypothesis that Lipitor is in normal competitive advertising after the bad news.
9
Conclusion
We study the advertising behavior of Crestor’s competitors following a petition of withdrawal of
Crestor. Our empirical work shows that the advertising of Crestor’s major competitor, Lipitor,
exhibits certain pattern which differs in a significant way from Crestor’s other competitors. To be
explicit, we find that Lipitor increases its advertising the most among physicians where Crestor
has taken a medium share in total prescriptions. Moreover, such changes in advertising patterns
28
are not found with the second and third seller, Zocor and Pravachol. Neither does Lipitor shows a
similar change in its advertising with physicians where other Statin drugs take a medium market
share. These results thus favor the strategic view of Lipitor’s post-news advertising and argue
against the normal competitive view.
Our empirical results are consistent with the predictions in Chen and Tan (2011), where they
argue that a predatory intention can underlie Lipitor’s post-news advertising. They show that under
asymmetric information between Lipitor and Crestor, in equilibrium Lipitor upward distorts its
advertising in order to induce Crestor to deem staying with certain physicians unprofitable. When
Crestor gradually ceases relationship building with those physicians, Lipitor can benefit the most
from the bad news by maximizing its profit in the long run. Moreover, the upward distortion
in Lipitor’s advertising occurs only among physicians where Crestor has taken a medium market
share, since for the rest of the physicians it is either unnecessary or impossible for Lipitor to induce
the “exit” of Crestor.
In contrast to the extensive theoretical studies on the use of predatory strategies to induce
a rival to exit, empirical study on predation is still rare. One reason for the lack of empirical
work relies with the difficulty in differentiating a predatory behavior from a normal competitive
behavior. Our work is an attempt to fill this blank by empirically testing predatory behavior.
Instead of directly comparing marginal benefit and marginal cost of advertising, our work detects
predation by linking firms’ strategic incentive to market properties. Our approach is based on the
“natural experiment” method. Since the news event is an exogenous shock to the Statin market,
we can detect predatory intent by comparing the changes in advertising from before to after the
news across different physician markets where the incentive to prey varies. While our work offers
a potential avenue of empirically testing predatory behavior, future work is certainly warranted to
further differentiate a predatory behavior from a normal competitive behavior.
29
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31
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32
Table 1. Major Brand of Statin Drugs
1
1
Brand
Content
Method
Type
Generic Available
Maker
Market Share
Advicor
niacin/lovastatin
Fermentation
Branded
No
Kos
1.6
Altocor
lovastatin
Fermentation
Branded
Yes
Andrx
0.79
Crestor
rosuvastatin
Synthetic
Branded
No
Astra Zeneca
10.89
2012
Lescol/XL
fluvastatin
Synthetic
Branded
No
Reliant
4.38
2011
Lipitor
atorvastatin
Synthetic
Branded
No
Pfizer
43.15
2010
Lovastatin
lovastatin
Fermentation
Generic
Mevacor
lovastatin
Fermentation
Branded
Yes
Merck
1.12
Pravachol
pravastatin
Fermentation
Branded
No
Bristol-Myers Squibb
11.44
2006
Zocor
simvastatin
Fermentation
Branded
No
Merck
23.42
2006
Market Share=number of prescriptions of the brand/(total number of prescriptions in 2004).
Patent Expiration
2.7
Table 2. Summary Statistics of Monthly Prescription and Detailing Visits
Sample Size
Mean
Sd
Min
Max
25th quantile
median
75th quantile
Total Number of Prescriptions
616
20.54
10.39
10
75
Crestor Market Share
616
0.09
0.13
0
0.94
0.07
0.11
0.20
Lipitor Market Share
616
0.43
0.20
0
1.00
0.29
0.42
0.58
Zocor Market Share
616
0.24
0.16
0
0.91
0.14
0.24
0.36
Pravachol Market Share
616
0.13
0.13
0
0.73
0.08
0.14
0.22
Share of New Patients
616
0.10
0.12
0
1
Share of Older Patients (Age 65+)
616
0.47
0.20
0
1
Share of Asian Patients
616
0.02
0.07
0
1
Share of Female Patients
616
0.47
0.16
0.09
1
Share of Severe-symptom Patients
616
0.06
0.1
0
0.83
Number of Crestor Detailing
616
1.29
1.48
0
11
Number of Lipitor Detailing
616
0.83
1.12
0
6
Number of Zocor Detailing
616
0.89
1.15
0
5
Number of Pravachol Detailing
616
0.58
0.95
0
6
1
Brand Market Share=number of prescriptions of the brand/(total number of prescriptions). The quantile market share of a brand is calculated using markets with non-zero prescriptions
of that brand.
2 Number of Brand Detailing is the number of physician office visits by sales representatives of that brand.
\
Table 3. Difference in Difference Result I, Changes in Lipitor Detailing
1
First Dif
[1]
[2]
2
Before
After
3
Median Crestor share
Dif-in-Dif
[3]
[4]
[5]
[6]
[7]=
[8]=
[9]=
Before
After
Before
After
[2]-[1]
[4]-[3]
[6]-[5]
[7]-[8]
[7]-[9]
Low Crestor share
High Crestor share
Mean
0.76
0.92
0.73
0.81
0.82
0.89
0.16
0.09
0.07
0.07
0.09
Sd
0.90
1.22
1.06
1.10
0.97
1.13
1.18
1.04
0.95
0.18
0.24
N
126
126
176
176
61
61
1
The outcome variable is the number of Lipitor detailing . We use Crestor's market share in the first 8 weeks of 2004 to construct the control group and the comparison groups. The
control group includes physicians with medium Crestor market share [0.05-0.18], and the comparison groups include physicians with low Crestor market share [0-0.05) and physicians
with high Crestor market share (0.18-1].
2 Before represents week 5-8 (February), the period before the bad news.
3 After represents week 10-13 (March), the period after the bad news.
Table 4. Baseline Results for Lipitor
Specification 1
Specification 2
Specification 3
Specification 4
Coef
Sd
Coef
Sd
Coef
Sd
Coef
Sd
CM 1
0.12***
( 0.03 )
0.31***
( 0.04 )
0.09***
( 0.03 )
0.29***
( 0.04 )
CH
0.02
( 0.04 )
0.4***
( 0.05 )
-0.11***
( 0.04 )
0.48***
( 0.06 )
CM*Postevent
0.07**
( 0.03 )
0.05
( 0.04 )
0.08***
( 0.04 )
0.07
( 0.04 )
CH*Postevent
-0.13***
( 0.05 )
-0.11***
( 0.05 )
-0.05
( 0.05 )
-0.12***
( 0.05 )
Postevent
0.06***
( 0.02 )
0.05***
( 0.02 )
0.05***
( 0.02 )
0.07***
( 0.02 )
Random Effect
Yes
Fixed Effect
Additional Control
Sample size
Yes
Yes
2
616
408
Yes
Yes
Yes
616
408
***,**, * denotes statistical significance at the 1 percent, 5 percent and 10 percent level respectively.
1
CM represents medium Crestor market share [0.07-0.2]; CH represents high Crestor market share (0.2-1]. Market share is based on
prescriptions in the previous month.
2 Additional control variables include: total number of prescriptions (market size), share of new patients, share of older patients (age
65+), share of Asian patients, share of female patients, share of severe-symptom patients.
Table 5. Baseline Results for Lipitor: Small vs. Large Markets
Specification 1
Coef
Sd
1
Panel 1: Small Markets
CM
2
Specification 2
Coef
Sd
Specification 3
Coef
Sd
Specification 4
Coef
Sd
0.15***
( 0.05 )
0.22***
( 0.07 )
0.12***
( 0.05 )
0.2***
( 0.07 )
CH
0.22***
( 0.05 )
0.37***
( 0.09 )
0.09
( 0.06 )
0.47***
( 0.09 )
CM*Postevent
0.15***
( 0.06 )
0.47***
( 0.07 )
0.18***
( 0.06 )
0.5***
( 0.08 )
CH*Postevent
-0.28***
( 0.07 )
-0.15***
( 0.07 )
-0.19***
( 0.07 )
0.03
( 0.08 )
Postevent
0.14***
( 0.03 )
0.04
( 0.04 )
0.13***
( 0.04 )
0.08***
( 0.04 )
Random Effect
Yes
Yes
Fixed Effect
Additional Control
Yes
Yes
3
Sample size
Panel 2: Large Markets
304
206
Yes
Yes
304
206
CM
0.15***
( 0.04 )
0.49***
( 0.05 )
0.12***
( 0.04 )
0.44***
( 0.05 )
CH
-0.07
( 0.05 )
0.70***
( 0.08 )
-0.20***
( 0.05 )
0.78***
( 0.08 )
CM*Postevent
-0.01
( 0.04 )
-0.20***
( 0.05 )
-0.02
( 0.04 )
-0.22***
( 0.05 )
CH*Postevent
-0.05
( 0.07 )
-0.14*
( 0.07 )
-0.01
( 0.07 )
0.03
( 0.08 )
Postevent
0.04*
( 0.02 )
0.06***
( 0.02 )
-0.03
( 0.02 )
0.07***
( 0.02 )
Random Effect
Yes
Fixed Effect
Yes
Yes
Additional Control
Sample size
312
202
Yes
Yes
Yes
312
202
***,**, * denotes statistical significance at the 1 percent, 5 percent and 10 percent level respectively
1
Small markets represent physicians with no more than 20 total prescriptions in both January and February. All other physicians are
categorized as large markets.
2 CM
represents medium Crestor market share [0.07-0.2]; CH represents high Crestor market share (0.2-1]. Market share is based on
prescriptions in the previous month.
3 Additional control variables includes: total number of prescriptions (market size), share of new patients, share of older patients (age
65+), share of Asian patients, share of female patients, share of severe-symptom patients.
Table 6. Difference in Difference Result II, Changes in Lipitor Detailing
1
First Dif
[1]
[2]
2
Before
After
3
Dif-in-Dif
[3]
[4]
[5]
[6]
[7]=
[8]=
[9]=
Before
After
Before
After
[2]-[1]
[4]-[3]
[6]-[5]
[7]-[8]
[7]-[9]
Median Pravachol share Low Pravachol share
High Pravachol share
Mean
0.82
0.90
0.75
0.93
0.64
0.69
0.08
0.18
0.05
-0.10
0.03
Sd
0.93
1.12
1.08
1.25
0.94
1.03
1.04
1.15
1.03
0.18
0.20
N
157
157
126
126
80
80
1
The outcome variable is the number of Lipitor detailing . We use Pravachol's market share in the first 8 weeks of 2004 to construct the control goup and the comparison groups. The
control group includes physicians with medium Pravachol market share [0.06-0.20], while the comparison groups include physicians with low Pravachol market share (0-0.06) and
physicians with high Pravachol market share (0.20-1].
2 Before represents week 5-8, the period before the bad news.
3 After represents week 10-13, the period after the bad news.
Table 7. Difference in Difference Result III, Changes in Zocor and Pravachol Detailing
1
First Dif
[1]
[2]
2
Before
After
3
Median Crestor share
Dif-in-Dif
[3]
[4]
[5]
[6]
[7]=
[8]=
[9]=
Before
After
Before
After
[2]-[1]
[4]-[3]
[6]-[5]
[7]-[8]
[7]-[9]
Low Crestor share
High Crestor share
Panel 1: Outcome variable is number of Zocor detailing
Mean
0.81
0.94
0.72
0.98
0.79
1.07
0.13
0.26
0.28
-0.13
-0.15
sd
1.16
1.24
1.02
1.23
0.95
1.29
1.23
1.10
1.17
0.19
0.26
N
126
126
176
176
61
61
Panel 2: Outcome variable is number of Pravachol detailing
Mean
0.60
0.77
0.49
0.47
0.43
0.66
0.17
-0.03
0.23
0.20
-0.06
sd
1.06
1.20
0.76
0.81
0.69
1.05
1.38
0.76
1.07
0.16
0.23
N
126
126
176
176
61
61
1
The outcome variables are the number of Zocor detailing and the number of Pravachol detailing , respectively. We use Crestor's market share in the first 8 weeks of 2004 to construct the control
group and the comparison groups. The control group includes physicians with medium Crestor market share [0.05-0.18], and the comparison groups include physicians with low Crestor market
share [0-0.05) and physicians with high Crestor market share (0.18-1].
2 Before represents week 5-8, the period before the bad news.
3 After represents week 10-13, the period after the bad news.
Table 8. Difference in Difference Result IV, Changes in Zocor and Pravachol Detailing (Reduced Sample)
First Dif
[1]
[2]
2
Before
After
3
Median Crestor share
1
Dif-in-Dif
[3]
[4]
[5]
[6]
[7]=
[8]=
[9]=
Before
After
Before
After
[2]-[1]
[4]-[3]
[6]-[5]
[7]-[8]
[7]-[9]
Low Crestor share
High Crestor share
Panel 1: Outcome variable is the number of Zocor detailings
Mean
0.86
1.05
0.85
1.17
1.00
1.42
0.19
0.32
0.42
-0.13
-0.23
sd
1.19
1.38
1.17
1.37
1.00
1.54
1.41
1.20
1.58
0.30
0.48
N
63
63
93
93
19
19
Panel 2: Outcome variable is the number of Pravachol detailings
Mean
0.73
0.75
0.77
0.58
0.80
0.95
0.02
-0.19
0.15
0.21
-0.13
sd
1.26
1.08
0.90
0.94
0.83
1.36
1.08
0.52
1.57
0.25
0.42
N
63
63
77
77
20
20
1. The sample in Panel 1 includes only markets where Zocor's market share is above its median level. The sample in Panel 2 includes only markets where Pravachol's market share is above its
median level. The outcome variables are the number of Zocor detailing and the number of Pravachol detailing , respectively. We use Crestor's market share in the first 8 weeks of 2004 to construct
the control group and the comparison groups. The control group includes physicians with median Crestor market share [0.05-0.18], and the comparison groups include physicians with low Crestor
market share [0-0.05) and physicians with high Crestor market share (0.18-1].
2 Before represents week 5-8, the period before the bad news.
3 After represents week 10-13, the period after the bad news.
Table 9. Results for Zocor and Pravachol
Specification 1
Coef
Sd
Specification 2
Coef
Specification 3
Specification 4
Sd
Coef
Sd
Coef
Sd
Panel 1: Outcome Variable: Number of Zocor Detailing
CM
1
-0.06***
( 0.03 )
-0.22***
( 0.04 )
-0.04
( 0.03 )
-0.32***
( 0.04 )
CH
-0.12***
( 0.04 )
-0.26***
( 0.05 )
-0.12***
( 0.04 )
-0.22***
( 0.05 )
CM*Postevent
-0.14***
( 0.03 )
-0.14***
( 0.04 )
-0.14***
( 0.03 )
-0.01
( 0.04 )
CH*Postevent
0.05
( 0.04 )
-0.09**
( 0.04 )
0.17***
( 0.04 )
0.08
( 0.05 )
Postevent
0.23***
( 0.02 )
0.26***
( 0.02 )
0.21***
( 0.02 )
0.26***
( 0.02 )
Random Effect
Yes
Yes
Fixed Effect
Additional Control
Yes
Yes
2
Sample size
616
400
Yes
Yes
616
400
Panel 2: Outcome Variable: Number of Pravachol Detailing
CM
0.31***
( 0.03 )
0.37***
( 0.04 )
0.4***
( 0.03 )
0.4***
( 0.04 )
CH
-0.3***
( 0.04 )
-0.31***
( 0.06 )
-0.24***
( 0.05 )
-0.36***
( 0.07 )
CM*Postevent
0.02
( 0.04 )
-0.09***
( 0.04 )
-0.02
( 0.04 )
-0.09**
( 0.05 )
CH*Postevent
0.36***
( 0.05 )
0.41***
( 0.06 )
0.43***
( 0.06 )
0.47***
( 0.06 )
Postevent
0.07***
( 0.02 )
0.09***
( 0.02 )
0.05***
( 0.02 )
0.1***
( 0.02 )
Random Effect
Yes
Fixed Effect
Yes
Yes
Additional Control
Sample size
616
322
Yes
Yes
Yes
616
322
***,**, * denotes statistical significance at the 1 percent, 5 percent and 10 percent level respectively.
1. CM represents median Crestor market share [0.07-0.2]; CH represents high Crestor market share (0.2-1]. Market share is based on
prescriptions in the last 4 weeks.
2 Additional control variables includes: total number of prescriptions (market size), share of new patients, share of older patients (age
65+), share of Asian patients, share of female patients, share of severe-symptom patients.
Figure 1: Nonparametric Estimation of Lipitor Detailing
1.05
1
Number of Lipitor Detailing
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0
0.1
0.2
0.3
0.4
0.5
0.6
Quantile of Crestor Market Share
Before
After
0.7
0.8
0.9
1
Figure 2: Nonparametric estimation of Lipitor detailing
1
0.95
Number of Lipitor Detailing
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0
0.1
0.2
0.3
0.4
0.5
0.6
Quantile of Pravachol Market Share
Before
After
0.7
0.8
0.9
1
Figure 3: Nonparametric Estimation of Zocor Detailing
1.4
1.3
Number of Zocor Detailing
1.2
1.1
1
0.9
0.8
0.7
0
0.1
0.2
0.3
0.4
0.5
0.6
Quantile of Crestor Market Share
Before
After
0.7
0.8
0.9
1
Figure 4: Nonparametric Estimation of Pravachol Detailing
0.9
0.85
0.8
Number of Pravachol Detailing
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0
0.1
0.2
0.3
0.4
0.5
0.6
Quantile of Crestor Market Share
Before
After
0.7
0.8
0.9
1