Generic Drug Industry Dynamics

60 Pages·2002·7.03 MB·English

GENERIC DRUG INDUSTRY DYNAMICS
David Reiffen
formerly
Federal Trade Commission
currently
Department of the Treasury
and
Michael R. Ward
University of Illinois
February, 2002
ABSTRACT
Because of its unique institutional and regulatory features, the generic drug industry provides a
useful laboratory for understanding how competition evolves within a market. We exploit these features to
estimate certain structural relationships in this industry, including the relationship between price and the
number of competitors, and between drug characteristics and the entry process. Our methodology yields a
number of findings regarding industry dynamic effects. We find that generic drug prices fall with the number
of competitors, but remain above long-run marginal cost until there are 8 or more competitors. We also find
that more firms enter, and enter more quickly in markets with greater expected rents. The size and time paths
of generic revenues, rents and the number of firms are greatly affected by measures reflecting the expected
market size. Finally, we demonstrate how these structural estimates can be used to evaluate recent policy
changes toward the pharmaceutical industry.
We would like to thank Denis Breen, George Deltas, Haj Hadeishi, Ron Hansen, Dan Hosken and Paul
Pautler for their helpful comments on previous drafts of this paper. The views expressed in this paper are
solely those of the authors, and not of their respective organizations, and in particular, they do not necessarily
represent the views of the Federal Trade Commission, or any individual Commissioner.
I. Introduction
Both the economics literature and the business press suggest that a typical pattern for a “new”
industry (or what Jovanovic and MacDonald, 1994, call an invention) is to have an initial phase in which a
small number of firms each earn significant profits, followed by a phase in which rapid entry of new firms
leads to increased competition and dissipation of some of those profits. If costs are relatively stable, a
“permanent” zero-profit equilibrium emerges. Alternatively, subsequent developments (e.g., innovations that
increase optimal scale), may lead to a “shake out”, whereby only a few large firms remain.
While this pattern seems to characterize many industries, the length of time during which early
movers retain their profits, how prices adjust during the entry process, and the degree of shake out vary
widely across industries (Gort and Klepper ,1982). Because the factors that influence the timing of entry and
exit are idiosyncratic to each industry, empirical studies of this dynamic process tend to focus on a single
industry (e.g., Gisser ,1999, Klepper and Simons, 2000), and in some sense, constitute a single data point.
The generic drug industry provides a useful laboratory for understanding how competition evolves
within a market. One can conceptualize the output produced by generic manufacturers of each drug as
constituting a market.1 These markets have a number of attractive features. First and foremost, there are
a large number of individual markets within the same industry, so we have multiple observations on similar
dynamic processes.2 Second, the market begins when the drug’s patent expires, and hence the date at which
the “invention” occurs is known in advance and the size of the potential revenue in each market can be
projected with some accuracy. Third, entry occurs at discrete and observable points in time, so that the
1 For this reason, we use the term market interchangeably with generic drug or chemical. In this study, the three terms
simply refer to a prescription drug whose patent has expired. In particular, the use of the term market does not correspond
to its antitrust meaning. Note that we study competition among generic producers of a chemical and not between generic
producers and the branded producer.
2 Bresnahan and Reiss (1991) also look at industries in which there are multiple (in their case, geographic) markets
in each industry. The focus of their work is characterizing how the static equilibrium varies across markets, rather than
the dynamics within markets.
2
researcher can measure changes in the number of producers and the consequence of those changes on pricing.
Moreover, because entry requires FDA approval, firms must sink significant cost to apply for approval prior
to knowing when, or how many, rivals will enter the market. Hence, firms must determine if their expected
post-entry rents are sufficient to justify the costs associated with FDA application.
These factors enable us to impose restrictions on the functional forms that enable us to identify the
parameters in certain relationships of interest. The first of these relationships is the effect of changes in
available rents on the pattern of entry over time. The second relationship of interest is the effect of changes
in structure (i.e., entry) on rents. Because structure and rents are simultaneously determined, these
relationships need to be estimated jointly. In addition, because rents are not observable, estimating these
relationships require a calculation based on several additional relationships, such as the relationship between
generic revenue and market structure, and between price-cost margins and market structure.
While estimating the relationship between market structure and prices is a necessary component of
estimating the relationship of interest, the estimated effect of structural changes on price is also of
independent interest, since this relationship has been an area of on-going interest in the industrial
organization literature.3 This industry provides a good opportunity for estimating this relationship because
of its unusual institutional features. Specifically, in these markets, the number of producers who apply for
FDA approval to sell a drug depends on the expected rents in the market, and not on the price of the generic
drug at any specific time. Hence, for any drug, the number of FDA-approved firms at any point in time
likewise can reasonably be viewed as unaffected by the contemporaneous price. Of course, the reverse is
probably not true; one would expect generic prices to be influenced by the number of generic producers in
3 See Bresnahan (1988) for a discussion and analysis of this literature.
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the market. Consequently, in this industry, the estimated relationship between the number of firms and the
price can be viewed as a structural one.4
Taking advantage of the pooled time series/cross section (across 32 different chemicals) nature of
the data, we can use an iterative procedure to compute the relationships of interest. This procedure yields
a number of findings regarding industry dynamic effects. First, more firms enter, and enter more quickly
in markets with greater expected rents. The size and time paths of generic revenues, profits and the number
of firms are greatly affected by measures reflecting the expected market size. Second, consistent with
previous work, we find that generic drug prices fall with the number of competitors. Specifically, we
calculate that prices for the initial generic monopolist are 35% - 50% (or perhaps even more) above long-run
marginal costs. Generic prices decline with the number of producers and begin to approach long-run
marginal cost when there are 8 or more competitors. Finally, we find that the flow of generic industry
profits increase as revenues grow but begin falling after five to eight months, as more entrants compete away
price-cost margins.
Not only does the generic drug industry have features that make it a useful case study of industry
dynamics, our structural estimates also provide a basis for evaluating some policy-relevant questions. In
particular, we use our estimates to shed some light on the effects of two changes in government policy
towards the generic drug industry that took place in the 1990s. First, as detailed below, the FDA increased
their scrutiny of generic drug applications in mid 1989. While the policy may have allowed the FDA to
discover, and therefore reject, more sub-standard applications, it also raised the cost of obtaining an ANDA
for a qualified entrant. Our estimates provide a means of determining the effect of that higher cost on
generic prices. Second, during the mid 1990s, the FDA appears to have adopted a policy of simultaneously
4 Because of these unique features of this industry, the relationship between generic price and the number of producers
has been studied previously (e.g., Caves, Whinston and Hurwicz, 1991). These studies are discussed in detail below.
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approving several firms (shortly after the initial patent expired) for certain drugs.5 Our estimates suggest
that there are two consequences of this action. The first consequence is that prices will be lower for any
given number of initial applicants, since competition among generics emerges earlier. At the same time, this
change in policy reduces expected rents. Hence, if this change is anticipated by potential entrants, fewer
firms will apply for FDA approval, leading to higher prices over some longer time period. Our structural
estimates can be used to quantify the effects of this change, and thereby provide guidance to policy makers.
II. Background
Before it is able to begin manufacture and sale of a new chemical entity, a prospective manufacturer
must obtain FDA approval. The process of obtaining a New Drug Approval (NDA) from the FDA is both
expensive and time consuming. The manufacturer must demonstrate, through a series of clinical trials, that
the drug is safe and efficacious. It has been estimated that for the average drug which was first tested in
humans in the 1970s and early 1980s, its producer had spent over $65 million (in 1987 dollars) on
development, and an additional $48 million on clinical and other testing.6 In addition, the clinical trial
process took upwards of 7 years. These drugs typically reached the market in the 1980s.
Prior to 1984, producing a generic version of most existing drugs involved a similar application
process. Although the generic producer did not face the cost of determining which drugs were technically
feasible and economically viable, it still faced the hurdle of demonstrating the safety and effectiveness of
its version before it could obtain FDA approval. The 1984 passage of the Waxman-Hatch Act reduced the
regulatory burden for firms to gain FDA approval to produce and sell generic versions of existing drugs. To
gain FDA approval under the Act, a producer of a generic drug need only demonstrate bioequivalence to a
5For example, the FDA approved nine generic entrants simultaneously for naproxen in December, 1993 and three
generic entrants simultaneously for alprazolam in October, 1993.
6 See J. DiMasi, R. Hansen, H. Grabowski and L. Lasagna (1991). This figure represents the expected cost of a
successful drug, in the sense that it includes the cost of drugs which do not obtain an NDA.
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drug which already has been approved by the FDA. Under the Waxman-Hatch process, a manufacturer
applies for an Abbreviated New Drug Approval (ANDA) from the FDA. The ability to obtain an ANDA,
rather than an NDA, has reduced the cost of obtaining FDA approval considerably. As discussed below, we
estimate that the cost of applying for an ANDA (including the cost of the requisite testing) was about $1.3
million in the early 1990s (and somewhat lower in the period immediately following passage of the Act).
Not surprisingly, this expedited approval process has increased the number of firms producing
generic versions of previously-patented drugs. Cook (1998) reports that for 13 major drugs with patents
expiring between 1990 and 1993, 11 had generic entry within two months of patent expiration. In contrast,
she notes that in Caves, Whinston and Hurwicz’s (1991) study of pre-Waxman-Hatch entry (between 1976
and 1982), only 2 of the top 13 drugs had generic entry within one year of patent expiration.
While the Waxman-Hatch Act lowered the time and expense required to enter production of a drug,
one element of the decision confronting a firm interested in generic production did not change. Entry still
requires a significant up-front expenditure, with a payoff that depends on the FDA's decisions with respect
to that firm’s application, as well as the timing of FDA approval of rivals’ ANDA applications for that drug.
Moreover, the time it takes the FDA to process applications can be both considerable and variable. In the
vast majority of cases, the initial ANDA application is found deficient by the FDA, and the applicant is
required to conduct additional tests, or submit additional material. In fact, the typical approved applicant
has gone through 2 or 3 resubmissions before it obtains its approval. Hence, from the applicants perspective,
the time between the initial submission and FDA approval is quite variable. Scott Morton (1999) calculates
that between 1984 and 1994 the time between the initial application and approval of ANDAs has averaged
about 19 months, with considerable year-to-year variation.
In addition to the time it takes to obtain an ANDA, entering a generic market requires a period of
time to begin the production process, since an approved source of materials and adequate production
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facilities are required at the time of the application. In total, the applicant has to anticipate two to three years
elapsing from the time it begins preparing to enter until it can begin selling a generic drug.
III. Modeling Industry Dynamics
In this section, we discuss how certain institutional features of generic drug markets permit
estimation of the structural relationships describing the evolution of the markets for generic versions of
individual drugs. Two features of the entry process in this industry are important to understanding industry
dynamics. First, as discussed above, for a firm unaffiliated with the initial patent holder, the timing of its
entry into the market is not fully under the entrant’s control. Not only is the date of its approval by the FDA
uncertain, but in addition, each applicant does not know when, or how many, other ANDAs for that drug
will be approved. In this sense, potential entrants are simultaneously making their entry decisions (although
actual entry will be sequential).
A second feature of the industry that becomes important because of this uncertainty regarding timing
is that an individual generic entrant’s share of the aggregate generic profits is likely to be highly dependent
on when it gains approval relative to other generic producers of that drug. By definition, firms that gain
approval before rival generic firms are able to sell their product sooner, and will face fewer initial
competitors. In addition, there is some evidence that such firms earn greater profits even after their rivals
have entered.7
Together these two features create a kind of “lottery” for prospective entrants into producing a
generic version of a drug. If a firm obtains early approval, it is likely to earn a positive return on its
application-related costs, while firms obtaining approval later in the process are likely to lose money. Thus,
7 Although we do not have any direct evidence on this point, information from a variety of sources points to such a
relationship. In addition to anecdotal material from industry participants, Cook (1998) shows that sales are highly
concentrated among a few firms in each market; even in markets with more than ten competitors, on average the top two
generic producers sell more than 60% of the units. In addition, Bond and Lean (1977) and Berndt et al. (1995) provide
several examples of drugs for which the first entrant had a substantial advantage.
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in contrast to markets in which entry decisions are sequential and competition results in the last entrant
earning zero profit, here the number of firms adjusts until the average firm earns zero profit.8 Specifically
(assuming n applicants of equal ability), the expected profit for each firm from applying for an ANDA is
(1)
Where (cid:65) is total generic rent at time t with i firms in the market, (cid:68) is the probability that i firms are in the
it it
market at time t, A is the cost of applying for an ANDA, and (cid:36) is the discount factor. V is defined as the
present value of the stream of total expected rents for all generic producers of a drug. Setting equation (1)
equal to zero yields the zero-profit equilibrium condition that determines entry. Specifically, if the universe
of potential entrants is large, then n adjusts until (1) is equal to zero (ignoring indivisibilities in the number
of firms). This equilibrium condition is a useful tool for examining how changes in the competitive
environment affect the market for generic drugs. The goal of this study is to estimate the relationships that
form the basis of equation (1). This in turn allows us to study industry dynamics. Three specific
relationships need to be estimated in order to make these determinations.
a. What are the generic profits at different times and with different numbers of competitors?
b. How many generic firms will enter a given market?
c. What is the probability that a specific number of firms will compete at a point in time?
Because the number of entrants depends in part on per-firm profits, and per-firm profits depend in
part on price-cost margins, understanding the relationship between prices and the number of firms is relevant
to all three questions. For this reason, we first discuss this relationship. Questions b and c both turn on the
8 Consequently, in contrast to the markets examined here, in a market with sequential entry, changes in the profits
earned by the first entrant will not change subsequent firms’ incentive to enter. Another important difference between
generic drug markets (where entry decisions can be viewed as simultaneous) and other markets is that an exogenous
change in the number of competitors (e.g, due to a merger several years after patent expiration) can lead to higher prices
without inducing entry, even if firms outside the market have the same entry costs as the incumbent.
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interrelationship between the number of entrants and the rents to these entrants. That is, both the number
of applicants and the speed of approval can be affected by the profitability of becoming a generic producer
of a drug, while at the same time, profitability is affected by the number of entrants. Hence, as depicted in
sub-section B, our approach is to jointly estimate entry decisions and profitability, given the relationship
between generic price and the number of firms estimated in sub-section A.
A. Generic Price and the Number of Competitors.
As noted, our interest in understanding this relationship is first as an input in estimating the
relationship between generic profitability and the number of entrants, and second, in the general question
of the relationship between structure and pricing. While the latter question has been dealt with in other
studies, we are interested in a specific aspect of this relationship, not explicitly examined elsewhere: How
does the marginal effect of an additional competitor on a drug’s prices change with the number of firms who
already have an ANDA for that drug. To address this question, we estimate a regression of the form
(2)
where P is the price in the post-patent expiration period when there are i generic firms producing chemical
ik
k, and P is the price of the branded version of product k in the month prior to patent expiration.9 D is a
bk i
dummy variable that equals one when there are i generic producers of chemical k and zero otherwise, and
the X are variables representing demand or cost shifters for drug k.
kj
In principle, N is the maximum number of entrants observed in the data. In practice, we take N to
be the number of entrants such that the price effect of going from N-1 to N is negligible. Following this
procedure, our empirical results allow us to set N equal to 11. That is, in the data, increasing the number of
9 We use the branded price before patent expiration, rather than the contemporaneous branded price because the latter
is likely to be determined jointly with the generic price. In contrast, the branded price before there is any generic entry
is likely to be independent of the number of generic producers in future periods.
9
generic entrants from 10 to 11 never changes prices by an economically or statistically large amount. The
interpretation of (cid:34) is the ratio of the generic price when there are 11 or more generic producers to the
0
branded price that prevailed before patent expiration, if all other independent variables were equal to zero.
The other (cid:34), such as (cid:34) , are the increments in the ratio over (cid:34) when there are five firms in the market, rather
i 5 0
than 11. Because (cid:34) reflects the ratio below which additional entry does not lead to lower prices, we view
0
((cid:34) + (cid:69)(cid:40)X )P as the long-run marginal production cost of the drugs (where X is the mean value of each
0 j(cid:39)kj b,k (cid:39)kj
Xj for that k). Under this assumption, ((cid:34)i /((cid:34)0 + (cid:34)i + (cid:69)(cid:40)j X(cid:39)kj)) is a measure of the price-cost margin with i
generic producers.
Since other factors in addition to the number of generic producers might affect prices, we include
the X to adjust for some of these effects. The specific variables we include are:
j
– Multiple: A dummy variable which equals 1 if there were multiple branded products in the market prior
to patent expiration.10 To the extent these firms compete, pre-patent expiration prices would be lower for
any given level of demand, which in turn implies the ratio of marginal cost to pre-expiration branded price
would be higher, other things equal. Conversely, if the branded products were not competing with one
another (e.g., because a licensing fee allowed for the monopoly profit to be shared between the branded
firms), Multiple would have no effect.
- Uses: The number of ailments or indications for which each drug is used.
- Subs: The number of drugs that are therapeutic substitutes for the drug in any of its indications.
An increase in the number of substitutes (holding Uses constant) should reduce the pre-patent
expiration branded price, and hence lead to a higher ratio of marginal cost to pre-expiration branded price.
Conversely, holding the number of Subs fixed, we would expect that a drug with more Uses would have a
10 Multiple brands might exist before patent expiration if the patent holder licensed the patent to another producer
during the patent-protected period. This might occur if the two parties had some disagreement regarding which firm held
the patent rights and reached a licensing agreement in lieu of litigation, or if two firms held complementary patents. In
our sample, 7 of the 33 drugs had multiple brands prior to expiration.
10