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PRICE COMPETITION IN PHARMACEUTICALS: THE CASE
A fundamental question in industrial organization regards the relationship between
price and the number of sellers. This relationship has been particularly important in the
pharmaceutical industry where legislative changes were specifically designed to foster
competition. Previous research on the pharmaceutical industry has shown generic entry
has a mixed impact; generic prices fall rapidly with generic entry, whereas branded
prices tend to increase or decrease only slightly. Using more complete data, focused on
one segment of the pharmaceutical industryÐanti-infectivesÐwe find that the
relationship between pharmaceutical prices and the number of sellers is more like
that found in other industries. (JEL L11, L65, D4)
between 6 and 15, and fall by another 52% as
sellers increase from the 6 to 15 range to more
organization regards the relationship between
than 40. These results contrast with previous
price and the number of sellers (N ). The inter-
work on pharmaceuticals using more limited
est in this issue has been rekindled by substan-
data and showing little impact of entry on
tial recent work.1 This article uses an extensive
branded prices.4 The results here indicate
data set to provide an empirical analysis of the
instead that the effect of an increase in number
price±N relationship for antiinfective pharma-
of sellers on prices in pharmaceuticals is similar
ceutical products.2 The analysis shows that (1)
prices fall rapidly moving from one seller to a
few and (2) subsequent increases in the number
attracted scrutiny from both policy makers
of sellers continue to reduce prices, even when
there are numerous sellers.3 Prices fall about
have been a number of studies of pharmaceu-
83% as the number of sellers increases from 1 to
tical pricing behavior in general and the impact
of generic entry on branded and generic drug
prices in particular. Most of the previous stud-
*We thank Michael Baye, Dean Lueck, and seminar
ies have concentrated on small sets of drugs
participants at Chicago, LSU, Penn State, Texas A&M,
that have faced patent expiration across a num-
UCLA, the University of Texas, and the spring 1994 meet-
ings of the Industrial Organization Group of the NBER for
ber of therapeutic classes. Early studies focused
helpful comments. Financial support from Merck & Co.
on reduced form or semireduced form regres-
sion models that showed that branded prices
Wiggins: Professor, Department of Economics, Texas
and generic prices responded differently to
A&M University, College Station, TX 77843-4228.
Phone 1-979-845-7351, Fax 1-979-847-8757, E-mail
generic entry. In particular, these studies
showed that branded prices responded little
Maness: Senior Managing Economist, LECG, LLC, 2700
Earl Rudder Frwy., Suite 4800, College Station, TX
77845. Phone 1-979-694-5780, Fax 1-979-694-2442,
4. The analysis here builds on Caves et al. (1991) and
Grabowski and Vernon (1992) but covers a much larger set
of products, including all 98 in the anti-infective category.
1. See, for example, Applebaum (1982), Bresnahan
In contrast, Caves and colleagues used 30 products and
(1981), Bresnahan and Reiss (1991), Caves et al. (1991),
Grabowski and Vernon used only 18. Later studies also
Porter (1993), Reiss and Spiller (1989), and Suslow
use limited numbers of products. Frank and Salkever
(1986); portions of this literature are summarized in
(1997) have a sample of 45 drugs; Reiffen and Ward
(2002) uses 32 drugs. By using a larger number of products
2. Although our analysis concentrates on the relation-
concentrated in a single therapeutic category, we hope to
ship between price and the number of firms, there has also
determine how prices are affected by the number of sellers
been substantial work on the relationship between price
for an entire group of products and avoid thorny problems
associated with the heterogeneities in product use. The
3. It should be noted that there is little if any price effect
analysis here also provides a much more in-depth evalua-
when the second generic enters, but price effects become
tion of the competition between branded and generics not
significant as the third and fourth generics enter.
found in these earlier investigations.
# Western Economic Association International
to generic entry, and in some studies even
primarily treat acute conditions, and a single
increased (see Caves et al., 1991; Grabowski
prescription is usually enough to treat a condi-
and Vernon 1992; 1996). Frank and Salkever
tion, prescriptions make a natural measure of
(1992) developed a theoretical model to explain
quantity. However, anti-infectives are some-
the anomaly of rising branded prices in the face
what different from other therapeutic cate-
of generic competition. Their model posited
gories in that generic entry has historically
a segmented market where there existed two
been less costly than in other categories. The
segment that continues to buy the established
branded product after generic entry and a
price-conscious segment. Frank and Salkever
approval of generic products, had essentially
show that because of the segmented nature of
been in place for decades for anti-infectives. As
the market, entry likely makes the demand
a result, although other categories did not see
facing the branded manufacturer less elastic
substantial generic entry until after 1984, anti-
infectives had faced numerous generic entrants
and Salkever (1997) provide empirical tests and
for some time. In addition, many anti-infective
confirm that, consistent with the segmented
products, especially older ones, tend to be pre-
markets theory, branded prices rise and generic
scribed by generic name instead of the brand
prices fall in response to generic entry.
name, which hastens the acceptance of generic
Finally, a number of studies have attempted
to characterize the behavior of generic firms.
These factors have led some researchers to
Scott Morton (1999; 2000) finds that revenue in
predict that branded anti-infective prices may
the years before patent expiration is the most
respond differently from those in other cate-
important determinant of how many generic
gories. Early empirical research seemed to con-
firms enter a given market. She also finds
firm that result.7 Later empirical research has
that generic firms tend to specialize in certain
provided more mixed results, with some studies
showing a pattern of rising branded anti-
infective prices in the face of generic entry.8
response of generic prices to generic entry.
Our results are more in line with the earlier
This article investigates how prices decline
studies, showing significant impact on branded
as the number of sellers rises, evaluating the
ability of formal theoretical models to organize
A second difference in our data is that they
the data. The results show that although exist-
measure activity at the retail level. The use of
ing models organize some of the data regarding
retail prices raises two issues for our analysis.
price declines, there are important discrepan-
Previous research has pointed out that phar-
cies between the data and existing models.
macists may have a financial incentive to favor
We then characterize the differences between
generic dispensing, especially since the advent
the data and the models, providing a basis for
of managed care (see Grabowski and Vernon
continued theoretical and empirical work.
1996). However, our data end in 1990, when
There are a number of important differences
managed care was a much smaller factor than it
between the data we use and those of previous
is today. A second issue is to what extent retail
researchers. First, unlike previous research
prices can be used to analyze price competi-
tion among manufacturers. Given the intense
focused on a single therapeutic categoryÐ
anti-infectives. This focus on a single therapeu-
tic category provides a number of benefits,
drugs. See Lu and Comanor (1998), Scott Morton (1999,
particularly with regard to controlling for
6. Such acceptance may also occur because the U.S.
cost differences and demand differences.
Food certifies the manufacture of each batch of active
Anti-infectives in particular is a useful category
ingredient for anti-infectives, but not necessarily for
because they are primarily used for acute con-
ditions, thus making demand conditions more
7. See in particular, Schwartzman (1976, chapter 12).
See also Congressional Budget Office (1998, chapter 3).
uniform.5 In addition, because anti-infectives
8. Both Ellison (1998), and Griliches and Cockburn
(1994) find that average branded anti-infective prices
rise with generic entry, whereas Ellison et al. (1997) find
5. Several studies note that the demand and supply
significant responsiveness between branded and generic
conditions may be different for acute versus chronic
WIGGINS & MANESS: PRICE COMPETITION IN PHARMACEUTICALS
competition among retail pharmacies, and the
data are used to construct an annual price per
use of simple pricing rules that apply across
prescription for each seller of each product
most (if not all) drugs, price competition
(detailed data appendix available on request).
among manufacturers is likely to be reflected
Attention is restricted to anti-infectives to con-
trol for units of measure, cost, and regulatory
conditions across products. Doctors normally
econometric analyses of product differentia-
write an anti-infective prescription for a quan-
tion, including identifying separate effects on
tity of medication designed to cure a given
prices from increases in both generic and brand
infection. This practice makes the individual
name competition. This analysis of competi-
prescription the natural unit of measure for
tion between branded and generic products
anti-infectives. For other pharmaceuticals, in
shows (1) that it is important to distinguish
contrast, a pill or a daily dose might be more
between branded products sold by other inno-
relevant.11 The active ingredients for these pro-
vative firms besides the pioneer and unbranded
ducts are also commonly manufactured using
entry by traditional generic manufacturers; (2)
identical equipment, making costs more similar
branded entry by other innovative firms has a
for anti-infectives than across pharmaceuticals
different effect on pioneer prices than generic
as a whole. Still, the analysis that follows intro-
entry by strictly off-brand firms; and (3) there
duces several control variables to allow for cost
appears to be significant competition between
differences in a more sophisticated way than in
generic and brand-name sellers, including the
pioneer. Hence the results indicate that there
are three distinctive product groups in pharma-
review standards are also the same within this
ceuticals: the pioneer, branded versions of the
class of products, so that regulatory costs and
same molecule sold by other innovative firms,
standards are also similar. Hence cost differ-
and ordinary generics, and there is significant
ences are likely small, and we can control
price competition within and between these
adequately for different manufacturing tech-
niques for different groups of products (see
We briefly review some summary statistics,
a complete review is available in the data
appendix (available on request). The average
The data consist of retail-level pharmacy
price (1982±84 dollars) in the sample is $10.29
transaction data for all anti-infective products
with a standard deviation of $21.68. The smal-
over the 1984±90 period.10 The data provide
lest price is 0 and the largest price is $391.46.13
yearly observations on total expenditures
and quantity of prescriptions sold for the indi-
vidual sellers of anti-infective products. These
11. It should be noted that there may be different dos-
ing and different presentations, e.g. liquid versus tablet, but
that regardless of the dosing or presentation the standard
practice is to write a prescription to cure the infectionÐ
9. The IMS data we use for our empirical analysis do
which is why physicians tenaciously encourage patients to
not include rebates that are commonly paid by branded
manufacturers to managed care. Congressional Budget
12. The analysis introduces dummy variables for
Office (1998) notes that these rebates can continue and
each class of productsÐfor example, tetracyclines and
increase after generic entry, and thus not measuring
penicillinsÐand also uses separate dummy variables for
them can obscure an important source of price competition
year of introduction that allows a flexible, nonparametric
for branded manufacturers. Note, however, that wholesale
estimation of possible trends or other changes in cost over
level data from IMS possess the same weakness, and to the
time, reflecting increased complexity due to more sophis-
extent that such rebates are important, our results under-
ticated molecules or changes in manufacturing techniques.
estimate the degree to which branded manufacturers
These controls represent a significant improvement over
previouswork(see,e.g.,Cavesetal.1991,whouseadummy
10. The data were obtained from the National Pre-
variable by therapeutic class, which would be the same as
scription Audit of IMS America. Total expenditures and
imposing constant costs for all products in our sample).
quantity data are collected from a stratified random sample
13. The reader should note that there are seven obser-
of pharmacies and then used to construct nationwide retail
vations where total revenue is reported as zero and several
sales estimates. In the present analysis, these monthly data
observations where revenue is also extraordinarily high,
have been aggregated into annual series, forming a panel
both yielding outliers in pricing calculations. Reestimation
data set where the unit of observations are annual data for
without these observations does not qualitatively change
an individual product sold by an individual company.
The average branded price (constant dollars) is
$30.30 with a standard deviation of $45.77, and
the average generic price (constant dollars) is
$6.27 with a standard deviation of $6.91. The
number of sellers for a given product varies
from 1 (monopoly) to 61. The mean is 26.9
sellers. Our measure for the number of related
sellers has a mean of 31.6 and ranges between
0 and 112. We also calculated the Herfindahl
entity in each year. The mean HHI in the sam-
ple is 3,677, and the standard deviation is 2,669.
brand-name products, 13% are pioneer pro-
ducts. The number of observations is fairly
evenly spread across the sample period and
across IMS classifications. Erythromycins
(14%) and cephalosporins (9.6%) contain the
Almost half the observations (46%) are from
drugs introduced before 1962. In other years,
introductions vary from none in some years up
to 8.4% of the sample in 1974. Thus the data set
provides much detail and ample variation for
Anti-infective products are broadly catego-
rized according to the general molecular struc-
ture of the central active chemical entity, such
as penicillins, tetracyclines, erythromycins,
further disaggregated into specific molecular
entities or combinations, and the analysis
here focuses on the number of sellers of these
The first point on the horizontal axis corre-
individual products. Our data include 98 sepa-
sponds to drugs with one seller and the price
rately identified compounds. The analysis also
is averaged over all such products in the
considers competition among sellers of closely
sample, the second price is the average price
related molecules. Ellison et al. (1997) examine
for all two-seller drugs, and so forth.14
cross-product price competition in cephalo-
Figure 2 presents a similar view using HHI
sporins, and find mixed evidence of price com-
petition across products for these closely
The two panels of Figure 1 present two dif-
related antibiotics. In contrast, Stern (1994)
ferent looks at the data. A shows average prices
finds that in two of his four categories there
for all sellers, and B shows the average price for
is significant intermolecular substitution.
just the pioneersÐthe firms that originally
Accordingly, in the econometrics we allow
for intermolecular effects, though our results
drop in prices with the entry of the second
are more similar to Ellison et al. than to Stern.
through fifth sellers. Prices fall from the single
seller price of more than $57 to $9.46 when
metric specification, however, it is valuable
there are five sellers. This rapid decline in prices
to examine the simple relationship between
continues throughout most of the relevant
price and number of sellers. Figure 1 presents
these data. Although the econometric analysis
uses individual prices for each seller in a panel
14. With 98 product categories and 7 years of data,
data set, for clarity Figure 1 simply presents
there are 686 possible industry concentration outcomes.
average prices, taking the mean price of all
For example, there are 178 monopolies and 45 duopolies
(90 price observations) making up the first two points in
drugs with a particular number of sellers.
WIGGINS & MANESS: PRICE COMPETITION IN PHARMACEUTICALS
Prices of Products and Number of Competitors
range. B, focusing just on the prices of the pio-
continuing price decline in the reduced form
neer developer, shows essentially the same
that continues from only a few sellers to more
pattern. Hence, these data show a decline in
than 40. Focusing just on the pioneer pro-
both overall average prices and prices of the
We tried other specifications, and all the
The continuing price decline is well illus-
results are broadly similar. For instance,
trated through a series of simple regressions of
although our analysis concentrates on the
the price±N relationship, with progressive
price±N relationship, we also calculated
truncation of the sample on N from the left-
the HHI for each of our 98 chemicals. The
hand side. These regressions permit one to
mean HHI was 3,677 with a standard deviation
assess the persistence of the inverse relation-
of 2,669. The range was 1,082 to 10,000. After
ship between price and N. The results show
constructing the HHI, we then replicated the
that there is a statistically significant impact
regressions using the HHI in place of the num-
of the number of sellers on price, including
ber of firms. We ran a series of regressions,
when the sample is restricted to more than
30 sellers.15 Hence there is a significant,
16. It should be noted that there is a single outlier for
the pioneer products, which makes some difference in the
15. The coefficient on the number of sellers in the sim-
results. There appear to be data problems with the prices
ple price±N truncated regressions are as follows (t-statistics
for this drug, Vibramycin (PFizer: the pioneer doxy-
in parentheses): for regressions including all chemicals
cycline). If Vibramycin is included, the effect of N is insig-
(N b 0), b À0.47 (À21.42); for the regressions including
nificant for N ! 20, and significant and positive for N ! 30.
only chemicals with five or more competitors (N ! 5),
With Vibramycin removed, the result is a significant nega-
b À0.15 (À14.213); for N ! 10, b À0.13 (À14.46); for
tive coefficient for N ! 20 and negative but insignificant for
N ! 20, b À0.15 (À13.38); N ! 30, b À0.06 (À3.81);
N ! 30. We have not found other instances where Vibra-
mycin swings results in this way or other similar outliers.
truncating the Herfindahl from above. The
To formalize the Cournot prediction, sup-
coefficient remained positive and significant,
even when the regression was restricted to a
P(Q) a À bQ with constant marginal cost,
HHI of less than 2,000. Thus, decreasing con-
c ` a, and where Q represents total market
centration reduces price, even for chemicals
output. With N identical sellers, the market
price generated by the Cournot model takes
It is unlikely that manufacturing cost differ-
ences explain the observed differences in price.
Anti-infectives appear to exhibit constant
marginal production costs, and manufactur-
ing techniques are highly similar. There are
differences in presentation, such as tablets,
Note that the Cournot model predicts a linear
capsules, and suspensions, but these differ-
relationship between price and roughly the
ences cut across products and groups of pro-
could make a large, systematic difference is
equation (1) when there is a linear demand
for intravenous drugs, which we investigate
and constant marginal cost. Constant cost is
later. Hence manufacturing cost differences
highly plausible for anti-infectives because the
are unlikely to explain observed price differ-
raw ingredients are manufactured in batches
ences. Still it is important to control carefully
that are small relative to total output and pill
making is constant cost. Linearity of demand,
might affect the observed price±N relation-
however, is a stronger assumption suggesting
ship, requiring a more detailed econometric
that one can think of (1) as a Cournot-like out-
come rather than a structural test (see later
The Cournot model also assumes that firms
are identical. For many of the chemicals in our
analysis, especially those that have been off
patent for a number of years, this assumption
relationship between price and the number of
is plausible. The industry uses batch produc-
sellers, it is useful to rely on formal models to
tion technology characterized by constant
guide the analysis. The data summarized in
returns to scale. For this reason, most of the
Figure 1 show a continuous decline in price,
previous literature in the field also uses
ruling out a simple Bertrand model of price
the number of competitors as the key variable
competition. Recognizing this, two simple
determining the competitiveness of prices
alternative models of competition are the stan-
(see Stern 1994; Ellison et al. 1997 for instance).
Though the results reported use the number of
the entry threshold model of Bresnahan and
competitors as the key variable, we also ran
Reiss (1991). Standard Cournot oligopoly the-
regressions using the HHI. The results are
ory predicts that prices should initially fall
broadly similar to those reported. Thus the
quickly and then steadily approach marginal
assumption of identical firms does not signifi-
cost. A similar prediction is also provided by
cantly affect the empirical results.
Bresnahan and Reiss's entry threshold ratio
In our analysis N is the exogenous variable
method, which predicts a steady fall in variable
driving prices in equilibrium. We treat N as
profit margins. We compare the predictions of
this simple Cournot model to the alternative of
and other factors in the previous period or per-
a simple linear relationship between price and
iods, and price is then determined by the pre-
the number of sellers and to a more general
determined number of sellers in a particular
model that nests these alternatives.
consistent with the data and with previous
17. The coefficients on the HHI in the truncated regres-
research on the determinants of generic entry
sions are as follows (t-statistics in parentheses): for the
(see Scott Morton 1999; 2000). For example, a
whole sample, 0.003 (24.54); restricting the sample to
regression of N on sales in the year before
HHI ` 5,000, 0.001 (11.48); for HHI ` 3,000, 0.002
patent expiration (where data are available)
(5.214); for HHI ` 2,000, 0.005 (6.97); for HHI ` 1,000,
WIGGINS & MANESS: PRICE COMPETITION IN PHARMACEUTICALS
60% of variation of the number of sellers in the
postpatent period. Hence ex ante market size is
fixed effects provides a much richer set of con-
the key variable driving variation in N.
trols for cost and demand differences than the
It is also important to control for other fac-
existing literature. For example, Caves et al.
tors that might affect prices. One key factor is
(1991) consider products across therapeutic
possible competition from related products.
categories (such as cardiovasculars versus anti-
Ellison et al. (1997) found little evidence to
infectives) and simply use a single fixed effect
support such competition, and Stern (1994)
for anti-infectives, which is equivalent to
finds such effects in some categories. To control
imposing a restriction that all of the fixed
for these effects we included the number of
effects considered here are zero because the
other sellers in the appropriate broader cate-
entire data set consists of anti-infectives. Hence
goryofanti-infectives,suchaspenicillins,tetra-
the present study provides a large advance in
cyclines, and so forth. Brand recognition is also
the control of cost and demand factors in
important, and we included a dummy variable
studying pharmaceutical pricing relationships.
for versions of the same molecule sold by other
We used weighted least squares because the
innovative companies that had achieved brand
cells for different numbers of sellers had sharp-
ly different variances. We used as weights the
extensive analysis of branded, pioneer, and
inverses of the estimated standard deviation of
generic products. In contrast, different presen-
the residual for each value of the number of
tations of drugs do not differ sharply, except
competitors.20 The results from the estimation
injectable products are systematically more
are reported in Table 1. The results show
expensive to manufacture and distribute. We
general goodness of fit with a large F-statistic,
introduced an IV dummy to control for these
R2, and all of the key variables are highly
The model derived from equation (1) does a
differences that require fixed effects to control
good job of explaining the empirical relation-
for product age, product group, and year. Year
ship between price and the number of sellers of
of introduction fixed effects were included to
a given product. The results show that price is
control for differences in product age that
closely related to the inverse of the number of
could lead to differences in either demand
sellers of individual products, as predicted by
or cost. For example, physicians sometimes
the Cournot model. The parameter estimate for
use older products first in a course of therapy,
1/(N 1) is 28.70 with a t-statistic of 7.07. The
potentially affecting demand and price. Alter-
natively there may be a trend where manu-
declines when a firm loses its (patent-protected)
facturing costs of more recently developed
products are higher due to the more advanced
seller drops prices by nearly $5, the third by
nature of the products. Individual year of intro-
$2.39, the fourth by about $1.43, and the
duction fixed effects provide a flexible proce-
fifth by about $0.97. These price effects, more-
dure for accounting for such differences, and
over, are large relative to the mean price in the
we will compare the results of using such fixed
sample of $10.29. Hence, when there are more
sellers in a market the price falls sharply, but
In addition, products in different therapeu-
these price effects moderate quickly. Still, a
tic subcategories may differ in manufacturing
significant though small effect continues far
cost. Even though anti-infectives are generally
into the sample; going from 25 to 35 competi-
manufactured using similar techniques, there
tors is predicted to reduce prices in the struc-
could exist some differences across categories,
tural model by $0.31, or about 7.5%.
such as tetracyclines or penicillins. Accord-
It is worth noting several other results.
ingly, we include fixed effects for these groups.
Finally, fixed effects were included for individ-
economically and statistically significant
ual sample years to control for changes in
19. The data appendix provides summary statistics for
18. This information on brand names was obtained
from Scientific American Medicine. See section 3 for a
20. This method implies that separate weights are cal-
more complete investigation of our definition of brand
culated for each regression reported in the article. The
name and how brand name affects pricing and price
results were not qualitatively affected by using a common
set of weights, except as reported in the HHI regressions.
Regression Results Based on the Cournot Model
Notes: Dependent variable: real price per prescription. N number of sellers. NR number of sellers of related
products. HHI Herfindahl index. t-statistics are in parentheses. Real price is in 1982±84 dollars. Regressions are
weighted OLS. Weights are calculated by number of competitors and in each regression are equal to the standard
deviation of the residual for each number of competitors. An appendix, available from the authors, contains the fixed
coefficient for sellers of related products is
small, only 1.333, not quite significant at the
price of generic products in the sample is
95% confidence level. Hence, these data suggest
slightly more than $6, this variable indicates
that price effects are driven primarily by
changes in the number of sellers of the com-
pound in question, where we concentrate the
firmsÐover generics.21 The variable represent-
ing intravenous products also shows a substan-
Although we concentrate on sellers of indi-
tial price premium; the coefficient indicates
vidual chemicals, we do not interpret these
that such products are priced at $13.11 more
chemicals as being separate markets. Instead,
than other products, ceteris paribus.
we take the weaker position that taking the
number of sellers of related products into
pound in question (N ) has a large effect on
account makes little empirical difference.
price, the number of sellers of related com-
Hence it is appropriate to focus primary atten-
pounds (NR) has little effect. As noted, param-
tion on sellers of individual products.
eters predict a price decline of nearly $5 moving
Before turning to the other specifications it
is important to consider the fixed effects.23 The
number of sellers from two to three cuts prices
by another $2.50, and so forth. In contrast, the
22. Results with other specifications, such as using dif-
ferent functional forms for N and using the HHI in place of
the number of firms, yield broadly similar results.
21. The analysis in section III contains a more complete
23. A complete tabulation of regression results, includ-
ing the fixed effects, is available on request.
WIGGINS & MANESS: PRICE COMPETITION IN PHARMACEUTICALS
results show that only 9 of the 24 individual
indicate significant differences across subcate-
year of introduction fixed effects are statisti-
gories because a joint test restricting these
cally significantly different from the omitted
coefficients to zero is highly significant with
median (1973) at the 5% confidence level. How-
ever, an F-test reveals that these effects are
Finally, only three of the year fixed effects
jointly significant at the 1% level (F 14.38).
(1984±89) are significant at the 5% level.
Inspection of the coefficients shows that prior
Furthermore, there is no distinct trend in the
to 1973 all 11 are negative and after 1973 8 of 14
year coefficients, which indicates that the data
are positive. Nevertheless, there does not exist a
do not suggest a simple trend in price over the
simple trend in that there is considerable var-
iation in the individual year fixed effects over
The fixed effects show that it is important to
time, showing that this method provides a
control for cost and demand conditions. The
highly flexible method for accounting for pos-
rich set of fixed effects shows substantial non-
linear variation by year of introduction, and
there are generally small but significant differ-
included to control for vintage effects, such
ences in prices across subcategories. These
as increased quality for newer drugs, which
fixed effects provide a significant improvement
Lichtenberg (2001) demonstrates is an impor-
tant attribute of newer drugs relative to older
possible cost and demand differences. Hence
ones. To quantify these effects in a simple way,
the measured effect of competitors on price
we also ran the regression using an age variable
should not be the result of cost or demand
in place of year of introduction dummies. This
procedure also permits us to test whether our
Turning to the other specifications, an alter-
more general treatment of vintage effects mate-
native to the highly nonlinear, inverse specifi-
rially affected the results. Age is defined as
cation of column (1) of Table 1 is a linear
years since introduction of the chemical. We
relationship. To test between these specifica-
also include age squared to account for poten-
tions, one can nest the two models including
tial nonlinear vintage effects. This parametric
both the linear and nonlinear terms, as is done
treatment of age is more restrictive than the
in column (2) of Table 1. The results for the
nested model indicate that both terms are
constrains age effects to a quadratic form. The
significant, suggesting that prices fall faster
results, however, are essentially unchanged
than the simple Cournot-like model would pre-
for all the key variables. These results also
dict. Furthermore, although the linear terms
ensure that the specification of age effects
are significant, they are economically small.
does not account for any differences between
For example, as the second firm enters, the
the results here and those of other investiga-
primary model predicts a price decline of
tors. The estimate of the key coefficient on
about $5, whereas the nested model predicts
1/(N 1) in this specification is 37.18 with
a somewhat slower price decline of just over
a t-statistic of 8.97. The coefficient for age
$3; these differences are fairly small so that
is À0.461 with a t-statistic of À7.14. The
both models organize the data fairly well and
squared term is small, 0.007, with a t-statistic
of 5.70. For drugs that first entered the market
The most important difference between the
during our sample period, the average first-
year price is $54.41. The quadratic age effects
regards their prediction of price declines when
thus predict a fall in price of little more than
there are large numbers of firms. The nested
13% over 40 years ($7.14). This parametric
model predicts a larger price decline when there
approach suggests that systematic age effects
capture better the empirical phenomenon of
continuing price declines far into the sample.
fixed effects, we omitted the median fixed effect
In fact, the linear term is roughly the same
for trimethoprim (IMS class 15500). Of the
size as the coefficient in the simple regressions
20 fixed effects for individual product groups,
of price on N when the sample is restricted to
only 10 are statistically significantly different
more than 30 sellers; hence, the linear term in
from this median. Even though the number of
the nested model enables it to capture the con-
significant coefficients is small, the data do
tinuing price declines far into the sample.
Another difference occurs in the effect of
with t 4.816). Thus, removing other HHI
the number of sellers of related compounds.
from the primary regression does not alter
In the inverse specification, these sellers have
the results in any way. We also repeated the
a marginally significant effect, whereas in the
regressions in column (1) and (4) using the
nested specification, these products have a
log of price as the dependent variable.
statistically insignificant effect. The small size
The results are qualitatively unchanged.25
of these effects and their sensitivity to the
The price±N regressions and the price±HHI
specification may help explain the disparate
regression represent two competing linear
findingsofStern(1994)andEllisonetal.(1997).
The third column of Table 1 shows the sim-
ple linear model without the nonlinear term,
tests to determine which model best explains
and the results are similar. The model exhibits
the data. The results were inconclusive. One
large F-statistics and R2 as in the other two
method to test these nonnested alternatives is
specifications, and the coefficients of all
to create an artificial nested modelthat includes
explanatory variables are of the right sign
both (HHI and other HHI) and (1/[1 N ] and
and statistically significant. The key difference
1/[1 NR]) as independent variables along with
is that the linear model does not capture the
all the control variables and then conduct an
sharp initial price decline that characterizes
F-test on the HHI coefficients and the N coeffi-
cients (see Greene 1993, 222±23). The results
indicate that neither specification can be
initial entry on prices of these various thera-
rejected. The F-test that the coefficients on
peutic compounds. This relationship is present,
HHI and other HHI are both zero is 4.21. How-
moreover, both in a simple reduced-form anal-
ever, in this specification, the coefficient on
ysis and in an in-depth econometric analysis,
HHI is not statistically significant (0.00009,
where control is introduced for product age,
with a t-statistic of 1.19), and the joint signifi-
potential manufacturing cost differences, the
cance may be driven by the significance of the
number of sellers of closely related products,
(À0.0003, t-statistic À2.21). The test that
We also ran these regressions with HHIs in
1/(1 N) and 1/(1 NR) are simultaneously
zero is also easily rejected (F(2,3110)
regression is reported in the final column of
21.69). We also conducted a J-test of the two
Table 1. The results show a good fit, with an
competing models to ensure that the results
R2 of 0.808, slightly less than for the regressions
were not affected by the control variables
using number of sellers. The coefficient on the
(see Greene 1993, 223). Using the J-test, the
HHI for individual chemicals is positive and
hypothesis that the HHI model does not add
significant, indicating that decreasing concen-
any explanatory power cannot be rejected, and
tration does reduce price. The coefficient on
the HHI for the other chemicals in the IMS
model is easily rejected. Thus, the J-test
class is negative and significant, with a coeffi-
seems to favor using the inverse of the number
cient equal in size to that of its own chemical
of competitors as an independent variable over
entity, indicating that decreased concentration
in the broader class results in higher prices.
It is interesting to note the similarities and
differences between these results and those in
HHI regression (column [4]) with other HHI
prior work. Perhaps the most closely related
removed. The results were almost identical
work in the literature on the price±N relation-
to those reported in Table 1, column (4) (the
ship is Bresnahan and Reiss (1991). They exam-
coefficient in this regression was also 0.0004,
ine prices for dentists, auto repair shops, and
the like in geographically isolated county
seats. Their key finding was that the largest
competitive impact occurs moving from two
24. One limitation of the approach is that it imposes the
Cournot model. A second alternative is to construct a gen-
to three firms, another large impact moving
eral conjectural variations model which nests the Cournot
conjecture and let the data determine if the Cournot con-
jecture can be rejected. We constructed such a model and
found (using nonlinear least squares) that the Cournot con-
25. An appendix containing the results of this regres-
WIGGINS & MANESS: PRICE COMPETITION IN PHARMACEUTICALS
from three to four, and smaller subsequent
price impacts. They also argue that their
inferred prices in relatively concentrated
pricing by recognizing the differentiated nature
of generic and brand-name products. Brand-
The analysis here shows a sharp price drop
as the number of sellers increases from one to
consciouscustomers,andgenericfirmscompete
four or five and much smaller price effects
over price. Competition within and between
thereafter, similar to their finding. Bresnahan
these segments can provide insight into pricing
and Reiss also find a significant difference
patterns and permits testing of specific models
of competition between these groups.
(county seats) and unconcentrated (urban)
markets. Their finding is consistent with our
are two reasonable alternatives. One way is
results, which show a continuing price decline
to categorize the original patent-protected
product, defined hereafter as the pioneer, as
In contrast, the analysis here shows different
results from those found in previous research
products marketed by other innovative compa-
on pharmaceuticals. Studies by Caves et al.
nies that are separately promoted and have
achieved significant brand recognition even
though they are not the pioneer. We evaluate
entry. The results here show that pharmaceu-
both alternatives. To separate products with
tical prices respond to an increase in the num-
significant brand recognition, we categorized
ber of sellers similar to price responses found in
products specifically listed by brand name in
other markets.There are several possible expla-
the Scientific American Textbook of Medicine.
nations for these differences in results.
Theseproductsaregenerallydetailedtodoctors
Perhaps the most important is that the sam-
in varying degrees and may or may not differ
ple here is much larger than that used in these
from more traditional generic entry.
prior studies and contains much more variation
in the number of sellers. In addition, we do not
names, and generics leads to several reduced
restrict attention to a short period following
form relationships between price and N. We are
patent expiration but instead consider a variety
particularly interested in: (1) average brand-
of products with sharply different numbers of
name prices and the total number of sellers,
sellers. We also control more tightly for cost
(2) average brand name prices and the number
and demand differences. A different type of
of brand name sellers, (3) average prices of
possibility is that the analysis here focuses on
pioneer products and the total number of sell-
anti-infectives. Such a focus contributes to bet-
ers, and (4) average generic prices and the total
ter control for cost and demand differences, but
number of sellers. These relationships are illus-
comes at the cost of a more narrow focus. For
trated in the four panels of Figure 3. Panel A
example, by concentrating on anti-infectives,
shows the relationship between average brand-
the analysis focuses on products for which a
name prices and the total number of sellers of
single prescription is designed to cure, usage
a chemical. A shows that the price of brand-
patterns are similar, and underlying cost con-
name drugs falls steadily with the number of
ditions are likely more similar. These factors
competitors until there are roughly 25 to
improve the econometrics but of course mean
30 competitors; afterward the point estimate
that extrapolation to other therapeutic cate-
indicates continued price declines, but the
decline is no longer statistically significant.
Panel B of Figure 3 provides a different look
analyses of the price±N relationship and
at brand-name prices by showing how the aver-
prior studies of pharmaceutical pricing. The
age price of brand-name products is related to
omission is that it does not take into account
chemical.26 The data show a considerable but
unbranded products. We now turn to an expli-
irregular decline in price as the number of
cit analysis of the differentiated nature of
pharmaceutical products and the effect of
26. Note that care must be used in interpreting these
figures. For instance, the first point in B does not represent
brand name sellers rises. Panel C shows that
These data show that increases in the num-
prices of pioneers steadily decline as the num-
ber of sellers of individual chemicals leads to
ber of sellers of the chemical rises. This panel is
price decreases over a large range, but the pat-
a repeat of Figure 1B. Note that the price
tern of decrease and average price levels differ
decline exhibited here is much like that of
significantly for branded and generic products.
Figure 1A. Finally, D presents data on average
For generics significant price effects continue
generic prices as related to the total number of
far into the sample, whereas prices stabilize for
competitors. The results show that for generic
brand names when there are large numbers of
products prices fall steadily until there are more
metric analysis of the competition between
an average monopoly price because there may still be com-
petition from generic sellers even if there are no other brand
27. The coefficients for the number of competitors is as
It is common in the industry to view brand-
follows (t-statistics in parentheses): For brand-name
name products as being differentiated from
prices: N b 0, b À0.95 (À8.93), N ! 10, b À0.13
their generic competitors. Pricing patterns
(À3.84), N ! 20, b À0.12 (À2.62), N ! 30, b À0.03
after patent expiration and generic entry
(À0.38), N ! 40, b À0.02 (À0.13). For generic prices:
N b 0, À0.14 (À16.71); N ! 5, À0.12 (À14.17); N ! 10,
show that the price of the pioneer product
À0.12 (À13.78); N ! 20, À0.15 (À13.84); N ! 30 À0.07
remains much higher than the level of generic
(À4.40); N ! 40, À0.01 (À0.38). For the HHI regressions,
competitors for substantial periods of time. In
the coefficients are as follows. For brand names: overall,
b 0.006 (11.34); HHI ` 5,000, b 0.001 (2.88);
fact, previous empirical studies have found that
HHI ` 4,000, b 0.001 (1.873), HHI ` 3,000, b 0.002
prices of pioneer products tend to rise after
(1.584); HHI ` 2,000, b 0.004 (1.13). For generic prices:
overall, b 0.0006 (9.22), HHI ` 5,000, b 0.001 (10.72),
HHI ` 4,000, b 0.002 (9.20), HHI ` 3,000, b 0.002
(5.34), HHI ` 2,000, b 0.006 (7.23).
way to approach competition between branded
WIGGINS & MANESS: PRICE COMPETITION IN PHARMACEUTICALS
Notes: NG number of generic competitors. HHI Herfindahl index. t-statistics are in parentheses. Dependent
variable is real price is in 1982±84 dollars. Regressions are weighted OLS. Weights are calculated by number of
competitors and in each regression are equal to the standard deviation of the residual for each number of competitors.
Fixed effects dummy coefficients are not reported. Regressions only include cases where market has a single brand-name
seller and one or more generic sellers. Regressions are based on 565 observations meeting these conditions.
and generic products is to view them as differ-
than generic prices. Column (1) of Table 2
entiated products. In this setting, additional
reports the results of the first specification.
generic entry should have a larger impact on
The control variable coefficients are qualita-
tively similar to those in section II. The
because entry is taking place closer to existing
F-statistics and R2 are large. The primary coef-
generic competitors in product space.
ficients of interest in column (1) are the coeffi-
Table 2 presents several specifications of the
cient for the inverse of the number of sellers,
relationship between price declines for branded
and the variable interacting this variable with
and generic drugs. Because generic drugs are
brand name. The inverse of the number of sell-
likely to compete more closely with each other
ers has the incorrect sign but is not statistically
than with branded products, one might expect
significant. In contrast, the results show that
branded prices to fall more slowly than generic
generic entry lowers brand-name prices.
prices when additional generics enter. Such a
The second column in Table 2 reports a loga-
possibility is also consistent with some of the
rithmic specification, and the third reports a
empirical works described. To address this
linear specification. The final column in
issue in detail, we postulate several specifica-
Table 2 uses HHI in place of a functional
tions where one variable captures the overall
form based on the number of competitors.
price decline and a separate variable captures
All four specifications show a significant effect
the differential decline in the price of branded
of generic entry on brand-name prices but little
effect of generic entry on generic prices. The
The first column in Table 2 is derived from
result is that the analysis is that the data indi-
a formal model of spatial competition (see
cate faster price declines for brand names than
Wiggins and Maness 1998). The specification
generics, contrary to the conclusions of pre-
is very similar to the Cournot analysis. The
other specifications relax the highly specific
In addition, we examine several nonstruc-
functional form to test the qualitative result
tural alternatives. These models also permit
that brand-name prices should fall more slowly
Nested Nonspatial Models of Product Differentiation
Notes: Dependent variable price. NG number of generic competitors. NB number of brand-name competitors.
t-statistics are in parentheses. Dependent variable is real price is in 1982±84 dollars.
branded sellers affect prices, in addition to
the impact of generics on branded prices,
sell the incremental ``branded'' products (so-
called branded generics) in the sample. To
the extent that branded entry causes larger
model for a ``segmented market' in pharma-
price effects, the results indicate that branding
ceuticals with two groups, a loyal group of
leads to more effective price competition by
price-inelastic brand-name consumers and a
incrementally reducing prices in the branded
relatively price sensitive group of consumers.28
They argue there is little competition between
these segments. In such a case, the prices of
regressions investigating these hypotheses. A
brand-name products will be unaffected by
pooling test was conducted to see if the three
the number of generic competitors and, sym-
metrically, the number of brand-name sellers
respect to the effect of the number of sellers,
ought not affect generic prices. An alternative
and the results reject pooling. Subsequent pool-
view is that all products besides the original
ing tests were conducted for pooling pioneers
pioneer are generic. This view is implicit in
and other brand names and for generics and
much of the empirical literature, which gener-
brand names, and in each case pooling was
ally examines only the pioneer's prices, implic-
rejected. Accordingly the econometric results
itly treating all entry as generic.29 This
will analyze the separate effects of branded and
approach implies that pioneer products repre-
The regression results permit an in-depth
analysis of the nature of competition between
Our approach, in contrast, separates prod-
brand-name and generic sellers and the effect
on prices. In the brand-name price regression,
both brand name and generic entry affects
28. See also Grabowski and Vernon (1992). Frank and
Salkever (1997) provide empirical support for the segmen-
ted market hypothesis by demonstrating that branded
30. Due todatalimitations,theunreportedfixedeffects
prices tend to rise with generic entry while generic prices
discussed earlier are pooled across all three types, pioneer,
other brand name, and generic. To the extent that these
29. See, for example, Grabowski and Vernon (1992),
fixed effects represent cost and demand differences among
Caves et al. (1991), and Frank and Salkever (1997).
chemicals, this specification is correct.
WIGGINS & MANESS: PRICE COMPETITION IN PHARMACEUTICALS
prices, but the coefficient on brand-name
products is about ten times as large as the coef-
ficient on generic products and is the incorrect
This article has provided an empirical inves-
sign. Nevertheless, the results show significant
tigation of the relationship between price and
competition between these product groups,
the number of sellers in pharmaceuticals. The
analysis used a data set covering all anti-
market hypothesis and with the notion that
infective products and showed that initial
all sellers other than the pioneer should be
entry led to sharp price reductions, with prices
falling from the range of more than $60 per
Turning to the generic price equation, the
prescription for single sellers, $30 when there
results clearly indicate that generic firms do
are two or three sellers, and less than $20 when
respond to brand-name entry. Note, however,
there are four or more sellers. The results also
there are important differences in how generic
show that prices continued to decline with
prices respond to other generic as opposed to
additional entry, eventually approaching $4
brand-name entry. The linear term for generic
for products with more than 40 sellers.
entry is large and significant, whereas the
The results show that increases in the num-
inverse term is small and not significant. For
ber of competitors significantly reduce prices,
brand-name entry the response is similar, but
even when there are numerous sellers. These
the effect of brand-name entry on generic prices
results tie in nicely with those of Bresnahan
is substantially larger (À0.88 per brand-name
and Reiss (1991). Using small, isolated county
entrant versus À0.16 for an additional generic
seats, they find that the competitive effects of
entrant). The results once again show signifi-
entry diminish after the third or fourth entrant
cant competition between branded products
but that prices stabilize above those in uncon-
centrated urban markets. The results here show
The results for pioneer productsÐproducts
a similar, rapid initial price decline, and then go
sold by the original developerÐalso contrast
on to show a continuing steady decline as the
with the segmented market hypothesis.
number of sellers rises from a few to many.
Generic entry has a large and statistically sig-
nificant effect on pioneer prices, both for the
linear and nonlinear terms. Hence pioneer
observed pricing pattern. The analysis showed
products do indeed lower prices quite signifi-
prices broadly consistent with Cournot quan-
cantly in the face of additional generic com-
tity setting, though prices declined more with
petition.31 Furthermore, there is suggestive
large N than that model would predict.
The analysis also ties into the emerging work
entry also effects pioneer prices in that the
on pharmaceutical prices. The analysis here has
linear term is quite large and statistically sig-
extended that work in several directions. One
nificant, although the inverse of the number of
direction is to provide a much more compre-
brand-name sellers has the wrong sign.
hensive analysis of pricing in a specific, impor-
Hence the results support the argument that
tant therapeutic category. Our results show
there is significant competitive interaction
substantial price sensitivity and stand in
contrast to results found by Caves et al. and
firms. These results indicate considerable com-
Grabowski and Vernon. There are several pos-
petition and show that increases in the number
sible reasons for these differences. One is that
of sellers in any segment generally reduce prices
the analysis here relied on all anti-infectives,
in the remaining segments. This competition
not restricting attention to the period closely
following patent expiration. The greater varia-
tion in the number of sellers also provides a
richer data set particularly because the analysis
used data exclusively since 1984, when there
were large numbers of generic sellers. This
31. There are several possible reasons for these differ-
ences from Grabowski and Vernon. Perhaps the most
important is that we have a much larger sampleÐthey con-
Waxman-Hatch Act, which eased the burdens
sidered only 18 products. It is also possible that their results
of generic entry. A second possible reason is
are confounded due to difficult to control for cost differ-
ences, or that our results are special due to the differential
that anti-infectives may be more price-sensitive
than other segments of the pharmaceutical
industry. This possibility, of course, means
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