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. 
 

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

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-profitequilibrium emerges. Alternatively, subsequent developments (e.g., innovationsthat

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
 

3

 See Bresnahan (1988) for a discussion and analysis of this literature.

3

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
 

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.

4

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
 

5

For 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.

5

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
6

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
 

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. 

7

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,
 

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.

8

(1)

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 

Where Ait is total generic rent at time t with i firms in the market, Dit is the probability that i firms are in the

market at time t, A is the cost of applying for an ANDA, and $ 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
 

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

(2)

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

where Pik is the price in the post-patent expiration period when there are i generic firms producing chemical

k, and Pbk is the price of the branded version of product k in the month prior to patent expiration.9 Di is a

dummy variable that equals one when there are i generic producers of chemical k and zero otherwise, and

the Xkj are variables representing demand or cost shifters for drug k.

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
 

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

generic entrants from 10 to 11 never changes prices by an economically or statistically large amount. The

interpretation of "0 is the ratio of the generic price when there are 11 or more generic producers to the

branded price that prevailed before patent expiration, if all other independent variables were equal to zero.

The other "i

, such as "5

, are the increments in the ratio over "0

 when there are five firms in the market, rather

than 11. Because "0 reflects the ratio below which additional entry does not lead to lower prices, we view

("0 + E(jX' kj)Pb,k as the long-run marginal production cost of the drugs (where X' kj is the mean value of each

Xj

 for that k). Under this assumption, ("i

 /("0

 + "i

 + E(j X' 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 Xj

 to adjust for some of these effects. The specific variables we include are:

– 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
 

11 In other studies of generic drug competition, it has been assumed that the generic price decreases with the number

of firms (Frank and Salkever, 1997), the number of firms and the number of firms squared (Caves, et al., 1991), or the

number of firms and 1 divided by number of firms (Wiggins and Maness, 1996). These papers are discussed at greater

length in Section V. 

11

higher pre-patent expiration branded price (all else equal), and hence a lower ratio of marginal cost to preexpiration branded price.

– Time: Number of months since patent expiration. Reflects any effect due to the passage of time, rather than

generic entry per se. For example, if there were costs savings associated with learning-by-doing, the sign

on Time would be negative. 

– RGrowth: The average monthly change in revenue during the year prior to patent expiration. We include

this as a proxy for post-expiration demand growth, which may influence generic prices. It is plausible that

higher revenue growth is associated with higher generic prices. 

This specification has several noteworthy features. Using dummy variables for the number of

generic producers imposes no constraints on the structure of the relationship between price and the number

of competitors. Hence, we allow the marginal effect of an additional firm to change in an unconstrained

manner with the number of competitors. This contrasts with some previous work, in which a specific

structure on the relationship is assumed (e.g., where the number of firms, and/or some explicit function of

the number of firms appear as explanatory variables).11 Each such specification makes implicit assumptions

about the pattern of price effects that can result from entry. For example, the implicit assumption made when

the number of firms is used as an explanatory variable is that the effect of an increase of one in the number

of firms is the same regardless of the initial number of firms. By allowing the marginal effect of an

additional firm to vary with the number of firms, we can examine questions such as how many firms are

necessary to lead to approximately marginal cost pricing. Allowing the marginal effect to vary is also

important to our goal of accurately measuring the rents associated with any specific number of generic

competitors. 
 

12 This criticism dates back at least to Demsetz (1973). For more formal analysis, see Bresnahan (1986).

 

13 That is, the panel structure allows us to control for price effects that are specific to each drug. Other studies have

controlled for drug-specific effects by including market-specific dummy variables. Either assumption allows calculation

of the average effect of increasing the number of competitors in a market. In addition, Frank and Salkever (1997) and

Caves, et al. (1991) use instruments for the number of generic firms.

12

As noted above, this relationship can be viewed as a structural one if one views the number of firms

at any time as exogenous. However, one standard criticism of empirical studies of the relationship between

market structure and prices is that structure is not exogenous, but rather is determined by the profitability of

entering the market.12 Hence, an observed negative relationship between prices and the number of firms in

the market might be due to the influence of concentration on prices (which is the maintained hypothesis of

much of the early work on this topic), but might also be due to some other factor that influences both prices

and structure. For example, if costs are higher in some markets, then the competitive prices (i.e., those that

yield normal rates of return) will be higher and the number of firms lower than in lower-cost markets.

As equation (1) illustrates, this issue also arises in generic drug markets, as the number of firms

applying for ANDAs adjusts in response to the available rents. However, the markets for generic versions

of drugs have several features which ameliorate this problem. First, because we have a time series of generic

prices for each drug, and demand and production cost probably do not change much over the three-year postpatent period, it is unlikely that prices and the number of firms in a market will be influenced by unobserved

shocks. Second, because a time-series/cross-section panel exists, one can control for drug-specific effects.

We do this by scaling the generic price by the pre-patent expiration branded price. This removes a significant

portion of the between-drug variation, allowing for a large number of observations.13 Finally, holding the

number of ANDAs constant, the number of competitors in each market at any point in time is, in part, a

function of the FDA review process, so that one might reasonably view the number of firms in each market
 

14The fact that most applications require one or more resubmissions suggests that the lag between initial submission

of an application and its eventually approval is stochastic. This implies that while the number of approvals is related to

the aggregate rents, the actual number of FDA-approved firms at any point in time may plausibly be independent of

contemporaneous price. 

13

(3)

at any point in time as exogenous (i.e., not a function of the contemporaneous price).14 Subsection B

explicitly models the entry process.

B. The Relationship between Structure and Profitability 

Equation (1) implies that in equilibrium, the number of generic producers of the drug will be a

function of the rents associated with that drug, and the difficulty of producing an application that is

acceptable to the FDA . This suggests that for the drugs in our sample, the number of firms that gain an

ANDA will be increasing in measures that are positively correlated with rents, such as branded revenues

prior to patent expiration, and decreasing in the cost of applying. We model this relationship between the

total number of firms applying for ANDAs to sell a drug and the characteristics of that drug using a Poisson

model. The use of the Poisson model implies that the number of applications corresponding to any specific

set of drug characteristics is stochastic, where the density of potential outcomes for the number of approved

firms, n, is given by

and : is the expected number of occurrences (i.e., applications for ANDAs) for a given set of values for the

exogenous variables. The value of : is estimated from a cross-sectional regression of the number of firms

that obtain ANDAs within 3 years of patent expiration (our measure of the number of applications) against

total rents and a measure of application costs. There is reason to believe that application costs varied over

time in our sample period. In particular, as discussed in Scott Morton (1999), it was discovered in 1989 that

some ANDAs had been fraudulently obtained. One result of this “generic drug scandal” was an increase in
14

(4)

(5)

the FDA’s efforts in investigating applications. This appears to have slowed down the approval process.

Following Scott Morton, we assume that application costs may have been higher after July 1989. This is

reflected in a dummy variable, which we denote Stringent, that equals 1 for the period after mid 1989.

Consequently, we estimate the relationship between : and the cost and benefit of applying as

where V is called the “exposure” and the exponential term reflect the rate at which the exposure leads to

entry. This structural relationship provides us a means to determine how the number of entrants adjusts to

changes in the costs and benefits of approval. It also provides us with a means of estimating the time-series

of entry within each market, since the expected number of producers at each point in time depends on the

total number of applications, as detailed below.

To represent the FDA approval process described in Section II, we assume that once a firm applies

for an ANDA, it faces an uncertain period of time before it gains approval. Specifically, we model the timeseries relationship between rents and entry by positing that there is a probability 8 of any firm j obtaining

an ANDA during month t. We assume 8 is constant over time (i.e., follows a hazard function with a constant

hazard rate). Since 8 is assumed constant over time, 1/8 is the expected length of time between application

and FDA approval. We estimate the following relationship for 8 as

We postulate that 8 may be increasing in V (which means that 22 > 0), either because firms apply earlier in

high V markets and/or have a greater incentive to file accurately. Since the value of 8 may also depend on

the regulatory environment; in equation (5), we portray this with by interacting the Stringent variable with
15

(6)

(1')

V. As discussed above, the FDA review process became more stringent in the period following the generic

drug scandal, so that we would expect a lower hazard rate for any given V during that period (i.e., 23 < 0).

Given 8, the total number of applicants for ANDAs in a market influences the number of firms with

ANDAs at each moment of time through the binomial formula. Specifically, define S = exp(-8t), as the

Survivorship function, where “surviving” means the applicant has not yet been approved (i.e., St is the

probability that a firm will not obtain FDA approval by month t). Then, using equation (3) along with the

binomial formula, the probability that i firms have ANDAs in period t is

Equation (3) and (4) characterize the effect of V on the expected number of firms applying for

ANDAs. At the same time, : has an effect on V through the impact of : (via equation (3)), and therefore on

the path of entry over time, as depicted in equation (6). To see the effect of the number of generic producers

at any t on V, recall the definition of V,

An implication of this characterization of equation (1') is that the effect of i on V is mediated through the

effects of i on expected margins and revenue. Changes in Dit change the probability weights associated with

each i. To the extent that margins and revenue change with the number of firms, the available rents will

likewise be a function of i. The estimate of the parameters in equation (2) therefore provide one necessary

component for determining rents. The other component required for estimating our system of equations is

a relationship between total revenues to generic producers and i. Our estimation of this relationship are of

the form
16

(7)

wherePi,k Qi,k is total monthly generic revenue in market k with i producers, Pb,kQb,k is the branded firm’s total

monthly revenue prior to patent expiration, and the Xjk are other variables that might affect generic revenue.

Given this relationship, we have a system of three structural equations (1, 3 and 5) in three unknowns

(V, : and 8). Because these equations are non-linear, we estimate them equations iteratively. Specifically,

our procedure starts with some initial values for 8 and : for each drug (we refer to these values as 8

0

 and :

0

).

These are used to calculate f(n), the density of nj, and the Dit according to equations (4) and (6). We then

calculate V using the calculated Dit and f(n) and the estimated coefficients from equations (2) and (7). Given

this V, we can estimate equations (3) and (5). The resulting estimates are a predicted :, :

^ and a predicted 8

8

^. If :^ and 8^ are sufficient close to :

0

 and 8

0

, we view the process as convergent; that is, we view these

values as the appropriate estimates. If the predicted : and 8 are sufficiently different than the initial values,

we then go through the same process again, calculating a new V based on 8

1

 and :

1

(where :

1

 and 8

1

 are set

equal to :

^ and 8

^ from the first iteration). The process continues until iteration z where :

z

 and 8

z

 are

sufficiently close to :

z-1 and 8z-1

. 

IV. Data

The previous section discussed the method we use to estimate the structural equations of interest.

As discussed there, the information required for these estimates includes data on prices, quantities, dates of

entry, and measures of market-specific variables that affect demand for each drug in our sample.
 

15 IMS Health, Generic SpectraTM

17

Our primary source for price and quantity data is IMS Inc., a proprietary vendor of information to

the pharmaceutical industry. The specific IMS data we used was their Generic Spectra data set.15 The IMS

data provided us with up to 6 years of monthly price and quantity data for the patent holder (3 years before

patent expiration, 3 years after) for a group of 32 drugs that went off patent in the late 1980s and early 1990s,

and subsequently faced competition from generic producers. The data on the generic entrants contains

monthly price and quantity data for each of the first three generic entrants, and similar data combining all

generic entrants, for the 3 years following patent expiration. A list of the drugs examined in the study and

the month and year they first faced generic competitors is provided in Table 1. 

The Generic Spectra data includes prices derived from two different sources; one based on product

shipments, the other based on price surveys. The product shipment source contains data on quantities

purchased (in kilograms of active ingredient) and expenditures made by pharmacies and hospitals for each

drug in our sample. The data are provided separately for each strength (e.g., 50 mg.) and form (e.g., oral

solid) of the drug. Our measure of price per kilogram is the average revenue derived by dividing total

revenue by quantity. 

The shipment-based data are derived primarily from purchases by pharmacies and hospitals, who

purchase from distributors, who in turn purchase from manufacturers. A small proportion, perhaps 5%, of

purchases are made directly from manufacturers. IMS captures these two types of sales differently. The

purchases from distributors are captured by IMS directly monitoring shipment data of a high percentage of

distributors (98% of all such shipments are contained in their sample). Direct purchases from manufacturers

are estimated from a sample of invoices. IMS then combines these two kinds of sales information.

The second set of price data in Generic Spectra is obtained from a sample of pharmacies. It includes

data on the average transaction prices paid by pharmacies, and the average realized selling prices received
 

16 Since the price data covered retail pharmacies, forms of the drugs which are not typically sold by pharmacies (e.g.,

injectables) are excluded from the price analysis.

 

17 For these two drugs, the most popular form of the branded production was an aerosol inhalant. Entry into the

generic production of this form came several years after generic entry into the drug forms reflected in our data. We

believe that the delay in developing a generic aerosol was due to an unexpired patent on the aerosol de livery system,

even after the patent on the chemical had expired. There also were unresolved issues related to demonstrating

bioequivalence of aerosol products to the branded versions. 

18

by pharmacies by drug strength and form. These selling prices are based on the actual transactions prices

per package (e.g., 100 pills) received by the pharmacies in the sample. According to IMS, the measured

acquisition price would reflect all relevant discounts, with the exception of year-end quantity discounts

provided by some manufacturers. We refer to these two series as pharmacy transaction prices. Some

summary statistics on the drugs in the Generic Spectra data set are provided in Table 2.

For drugs with multiple strength/form combinations, we faced the question of how to construct a

price series from these data. Our method was to use price data only on the strength/form of the drug that

generated the most revenue in the year prior to patent expiration, as long as that strength/form was available

as a generic.16 For all but two of the drugs the generic form of this strength/type was available; in most cases

it was the best-selling strength/type for both the innovator and the generic firms. For the two exceptions

(Metaproterenol and Albuterol), there was no generic version of the most popular form, so that the price data

recorded are for the best selling generic strength/form.17

The reason we chose the price for the best selling strength/form as the basis of our price analysis,

rather than the average price for all strengths is that with the latter, one observes changes in the price variable

whenever there are changes in the relative sales of different strengths of the drug, even if prices are

unchanged. The advantage of our approach is that it measures a change only if a price charged by the

manufacturer or pharmacy changes. The disadvantage is that we do not use information on price changes

for other strengths. The trade-off seems to favor our approach if the manufacturer does not anticipate

changes in relative sales volume, so that the forces changing relative shares are uncorrelated with those
 

18As Mouton (1986) observes, using multiple observations with essentially unchanged exogenous variables leads to

an downward bias in estimated standard errors. For this reason, we chose the conservative approach of taking only one

data point for each number of competitors in each market.

 

19A useful feature of the 3-year time frame is that the process of entering generally takes at least three years. Hence,

any firm that receives an ANDA in this time frame will have commenced the process prior to observing the ANDAs that

were awarded to other firms.

19

changing prices. On the other hand, if changes in relative demand are anticipated, then prices will move for

the same reasons as relative demand, and price changes for one strength may understate or overstate the

“average” change in prices. We thought that changes in relative sales of different strengths/forms of a drug

are likely to be unanticipated, and hence we choose to look at a representative price, rather than the average

price across strengths and forms.

Another issue we faced was what time period constitutes an observation. Specifically, should each

month constitute an observation, or should we take an average over all months in which the number of

generic producers did not change? The first approach implicitly treats each month as an independent

observation, which seems to be unlikely, and we instead chose to use the average price for all of the months

with the same number of generic producers as a single observation.18 This approach reduces the number of

observations substantially, which tends to reduce the statistical significance of our results, but as Tables 4-6

indicate, we are still able to find significant pricing effects.

Data on the timing of entry were collected from the FDA publication Approved Drug Products,

commonly referred to as the Orange Book. The Orange Book lists the date each firm received its NDA or

ANDA from the FDA. The Orange Book also enables us to determine if there were multiple branded

products prior to patent expiration. Because the Generic Spectra data is limited to 3 years of post-patent

expiration data, we limit our analysis of entry to ANDAs awarded within three years of patent expiration.19

Finally, we constructed two demand-side variables - Uses and Subs - to capture demand differences

across drugs. Uses measures the number of ailments or indications for which each drug is used. Subs
 

20 While the IMS provides information on the “therapeutic class” (e.g, cephalosporin antibiotics) to which each drug

belongs, these categories tend to be over-inclusive in that all drugs in the therapeutic class would not actually be used

for the same ailment, as Caves, Whinston and Hurwitz (1991) and Lu and Comanor (1998) have noted. Caves, Whinston

and Hurwitz (1991) further note that a suitable measure of substitution would have been helpful to their analysis, but

found such a measure difficult to construct. Scott Morton finds that the therapeutic class variable has little predictive

power in her regressions. Lu and Comanor (1994) follow a similar procedure to that used here and find that their

measure does have explanatory power.

20

measures the number of alternative drugs that are generally used to treat these same indications.20

The basic method used to create these variables was to first determine the indications for which each

drug is used. If a drug was available in both an oral and an intravenous (IV) or intramuscular (IM) version,

we confined the analysis to the oral version only. The primary source in determining indications was the

American Hospital Formulary Service (AHFS) Drug Information, 1996, augmented by AMA Guide to

Prescription and Over-the-Counter Drugs, and The People's Pharmacy. To the extent possible, we included

not only the FDA-approved (or labeled) indication, but significant unlabeled uses as well. This constitutes

our uses variable.

For each indication, we determined the other drugs which are also used for that indication. We

defined substitutes fairly narrowly, so that two patients with similar symptoms might reasonably be

prescribed either the drug in our sample or the substitute. One source of these data was the AHFS book. It

was developed as a formulary; its specific purpose is to provide the information hospitals need to determine

which drugs to buy, and which drugs primarily serve indications for which lower price alternatives are

available. This source was augmented by the AMA Guide to Prescription and Over the Counter Drugs. The

Guide has several dozen discussions of classes of drugs, and describes which drugs have similar mechanisms

of action. Finally, for two classes of drugs (hypotensives and antibiotics), we consulted a practicing internist.

Based on this, we constructed a variable called Subs by summing the number of substitutes in each

use over all of its Uses for each drug in our sample (counting each substitute only once, regardless of how

many uses it can be a substitute in). We then used the Orange Book to determine the date when each

substitute for the drugs in our sample came on the market. This enabled us to determine the number of
21

(2')

substitutes on the market on the day the first generic version of the product was available, and also whether

any new substitutes subsequently became available. 

V. Results

A. OVERVIEW

Figure 1 depicts the basic empirical method used to estimate the structural parameters of interest.

The presentation of our results in this section follows this blueprint. Subsection B presents our estimates of

the relationship between the number of producers and generic prices, as characterized in equation (2).

Because price effects both play a part in other results, and are of interest in of themselves, we examine the

robustness of those results by using several alternative price series. This subsection also includes a

discussion of relationships between generic prices and the number of competitors that have been found in

other studies. Subsection C presents the revenue regression depicted in equation (6). By combining the

estimates from equations (2) and (6), we can calculate the aggregate generic profits associated with any

specific number of generic producers.

As the diagram in Figure 1 indicates, V, : and 8 are jointly determined, so that equations (3) and (5)

were estimated using an iterative procedure. This procedure searches for a combination of V, : and 8 that

constitutes an equilibrium (i.e., so that the V calculated in iteration z results in the same : and 8 in iteration

z+1 as the : and 8 used to determine V in iteration z). We find that convergence occurs in the 28th iteration.

The resulting estimates relating to the time path of entry are presented in subsection D. Subsection E

presents our estimates of the equilibrium values of V for the 32 drugs we study.

B. PRICES AND STRUCTURE

1.Findings - The pricing equations we estimate are of the form 
22

Where Pi,k /Pb,k is the ratio of the price in the post-patent expiration period when there are i generic producers

of drug k, to the price charged by the branded firm prior to patent expiration. Di is a dummy variable which

equals 1 when there are i producers of drug k. 

We estimate equation (2') for 8 different price series. The first four regressions, which are reported

in Table 4, use the data derived from the sample of pharmacy prices. The first two regressions are based

on the transaction prices paid by pharmacies (which we call wholesale price), while the last two regressions

are based on the prices charged by pharmacies (retail prices). For both wholesale and retail prices, we

estimate equation (2') for both the average generic price and the price of the first generic entrant’s product.

 Table 5 is based on shipments by distributors and manufacturers. The prices used in these regressions are

derived by dividing total revenue by quantity shipped. We estimated these regressions separately for drug

stores and hospitals, and for both types of buyers we analyze both the average generic price and the first

generic entrant’s price. 

To interpret these results, first note that the intercept represents the ratio of generic price to the

branded price when the number of competitors is large, and all other independent variables are equal to 0.

For example, the estimate of 0.429 for the coefficient on the intercept in column (1) of Table 4 implies that

on average, the generic price would be about 43% of the pre-patent expiration price of the branded product

when there are 11 or more competitors, if all other variables were equal to zero. As discussed above, adding

the other variables (evaluated at their mean values) times the relevant coefficients to the intercept can

reasonably be viewed as an estimate of the ratio of marginal cost to the branded price, which here equals

.533. The interpretation of the other firm number coefficients, such as the coefficient on one firm ("1

 from

equation 2') is the increase in this ratio due to having fewer than 11 generic competitors. For example, the

coefficient of 0.287 on one-firm in column (1) of Table 4 implies that ratio of generic price to pre-patent

expiration branded price will be 0.820 (= 0.287 + 0.533) when there is a single generic firm. 
23

Note that, as one would anticipate, the "i generally decline with the number of competitors. Again,

using the example of the coefficient estimates from the first column of Table 4, the ratio of generic price to

pre-patent expiration branded price falls from .820 with one generic competitor to .762 with two generic

competitors, and continues to decline toward .533 as the number of competitors rises. However, given the

size of the is’ standard errors, one cannot draw conclusions about the incremental effect of an additional

competitor on equilibrium prices.

The implied marginal costs tend to be lower, and the firm coefficients higher in Table 5 as compared

to Table 4. For example, in the first column of Table 5, we find that the implied ratio of marginal cost to prepatent expiration branded price when there are 11 or more firms is about 21% of the pre-patent expiration

branded price (compared with an estimated ratio of about 53% in the first column of Table 4). However, the

estimate of the premium over the intercept associated with any specific number of firms (i.e., any specific

"i

) is higher than in the first column of Table 4. For example, "1 = 0.536 in the first column of Table 5,

yielding an estimate of the ratio when there is one generic producer of 0.745 (=0.536 +0.209), similar to that

in the first column of Table 4 (0.820 = .287 + .533).

While there are some differences across the eight regressions in regard to the magnitudes of the

pricing effects, the general picture is quite similar across equations. In every case, there is an economically

and statistically significant difference between the price when there is a single generic competitor, and the

price when there is a large number of generic competitors in the market. A price cost margin remains when

there are relatively few generic producers, but eventually the premium shrinks and disappears. In Table 4,

the coefficients for 7-10 firms are not statistically significant in any regression, and are typically less than

0.1. In Table 5, the coefficients sometimes remain statistically significant with up to 9 generic firms (and

in one case, up to 10), although the magnitude of the coefficients continue to decline as the number of firms

rises, and is generally in the positive 0.15-0.25 range for more than 6 firms.
24

Figure 2 presents a graphical representation of the estimated relationship between the average

wholesale generic price and the number of firms in the market from the first columns of Tables 4 and 5. The

results suggest a negative relationship between price and the number of firms. They also suggest that the

marginal effect of an additional firm tends to decline with the number of firms. 

Of the other variables, only Multiple seems to have an effect that is consistent across specifications.

Multiple has the anticipated positive sign in all of the regressions reported in Tables 4 and 5. Moreover, it

is statistically significant at the 5% level in 5 of the 8 regressions, and economically meaningful. For

example, the coefficient of 0.069 in column 1 of Table 5 implies that having multiple branded products prepatent expiration increases the ratio of generic price to branded price by 0.069. 

The signs of both the Subs and Uses variables tend to vary across equations. Subs has the predicted

positive sign in 4 of the 8 regressions, and is statistically significant at the 5% level in 4 regressions;

however, in 2 of these 4, the sign is negative. Uses has the predicted negative sign in only 2 of the 8

regressions. Moreover, in 4 of the 6 cases in which the sign is positive, it is statistically significant at the

5% level. Neither Time nor RGrowth is statistically significant in any regression, although both are positive

more often than they are negative. 

2. Comparison to Other Results

Estimating the relationship between price and industry structure has a long history in industrial

organization economics. One general criticism of the approach is that is implicitly assumes that structure

is exogenous, whereas in most industries it should be viewed as endogenous. As discussed in Section III,

the markets for generic versions of drugs have several features which ameliorate the endogeneity problem,

and hence make measuring the relationship between the number of firms and prices more meaningful. 

Perhaps due to these features, the relationship between the price of a generic drug and the number

of firms producing that drug has been examined in at least three previous studies. All three studies of which
 

21 In contrast, the relationship between the number of generic producers and the branded price is less clear. Caves

et al. (1991), Grabowski and Vernon (1992) and Frank and Salkever (1997), find that generic entry has a relatively small

negative effect on branded price. However, Wiggins and Maness (1996) find a rather large effect of generic entry on

branded prices.

25

we are aware use annual price and quantity data from IMS, and find a negative relationship between the

generic price and the number of generic competitors.21 

All three studies impose a specific functional form (e.g., in Frank and Salkever,1997, price is

assumed to be related to the number of firms), and consequently, the coefficient estimates cannot be directly

compared to ours. What we can compare is the predicted change in price resulting from a specific increase

in the number of generic producers. For example, our results from the first two columns of Table 4 imply

that an increase in the number of generic producers from 1 to 10 will reduce wholesale generic prices by 35

to 40%. The estimates in Table 5 are larger; the predicted price declines range from 50 to 70%. Previous

studies yield predicted values that fall between these sets of estimates. The estimates in Caves et al. (1991)

imply that when there is only one generic producer, price is about 40% below the pre-patent expiration

branded price, and declines by about 50% (to 70% below the pre-patent expiration branded price), when there

are 10 such producers. Using the estimates from Frank and Salkever (1997), one would estimate that an

increase in the number of generic producers from one to 10 would lead to a 45% reduction in price. Finally,

Wiggins and Maness’ (1996) estimate that an increase in the number of sellers (which includes both generic

distributors and manufacturers) from 1 to 10 would lead to a 48% decrease in average generic price (based

on Table 5 in their study).

C. REVENUE AND STRUCTURE 

Equation (7) relates the total revenue derived from generic sales to other observable characteristics

of the market. In contrast to equations (2) - (5), we are not primarily interesting in testing any hypotheses

about the individual parameters of equation (6). Rather, the main use of these results is in estimating V. The

specification we estimate is the following: 
 

22 The reason is that there are two offsetting effects of elasticity on generic revenue, holding pre-expiration revenue

constant. For any given price discount relative to the branded product, higher elasticity implies lower initial margins

(making discounts less profitable), but likely means that any given price discount will result in larger generic revenues.

26

(7')

The dependent variable is the natural logarithm of generic revenue. The explanatory variables include 

Ln(BrandRev) - the natural logarithm of total revenue for the branded product(s) six months before patent

expiration,

FrDrug - the percentage of branded revenue obtained through drug stores sales and other outpatient outlets

(as opposed to hospital sales) prior to patent expiration,

i - the number of generic producers 

% of Conv Insurance - The percentage of all insureds that are covered by a fee-for-service structure, as

opposed to some kind of managed care organization (MCO), and 

Subs, Uses, Multiple, Stringent, and Time are the same variables used in equation (2).

We expect J1

 to be positive and close to one; a given percentage increase in pre-patent expiration

revenue should lead to a similar percentage increase in generic revenue. Both J2 and J3 are proxies for

demand elasticity; an increase in the number of substitutes means that more alternatives are available (i.e.,

higher elasticity), while holding the number of substitutes constant, more uses will reduce elasticity.

However, because the effect of elasticity on generic revenue is ambiguous,22 the only prediction we can make

is that an increase in Subs has an opposite effect on elasticity of an increase in Uses, so that J2 and J3 should

have opposite signs. Similarly, the sign of J4 is ambiguous. On the one hand, hospitals are more likely to

switch away from the branded product to the generic version of that product (especially during the sample

period) than the individual consumers who purchase at drug stores, which suggests higher generic revenue

for drugs with higher hospital sales. On the other hand, competition between generic producers is likely to
 

23 The pricing evidence supports this conjecture; as Table 5 shows, the average hospital price approaches the

competitive level with fewer firms than the average drug store price.

 

24 We use reciprocals of the number of firms and time since patent expiration to better reflect the expected relationship.

The functional form chosen implies that the marginal effect of both time and the number of firms remains positive, but

decreases as both increase.

27

be more intense for drugs with a high hospital share, since hospitals are likely to be more willing to switch

between generic suppliers.23 This suggests that generic prices and revenues will be lower for drug which

more of their sales are made by hospitals.

We hypothesize that the sign on J5

 will be negative, since in markets which had two firms prior to

patent expiration, generic producers will have to face two competing branded producers, and all else equal,

will find it more difficult to make sales. It seems likely that the generic drug scandal would reduce

consumers’ perception of the quality of generic drugs. Hence, we would expect lower demand and revenue

for generic drugs in the post-scandal period, so that J6 would be negative. MCOs tend to have policies that

encourage the use of generic drugs. We would therefore anticipate that as the percentage of patients covered

by conventional insurance falls, generic revenue would increase, so that J7

 would be negative. We expect

J8 to be negative; total generic revenue should increase over time (and so decrease with the reciprocal of

Time), as purchasers become more familiar with the generic product.24 The effect of the number of

competitors (i.e., the sign of J10) is less clear; an increase in the number of generic producers reduces price

and increases quantity of generics sold, so that the net effect on total generic revenue of more competitors

depends on whether the (post-entry) demand elasticity is less than unity. We also include two interactive

terms; the ratio of Ln(BrandRev) to Time and the ratio of Ln(BrandRev) to the number of generic producers.

Table 5 presents the results of our estimation of equation (7). We estimated four alternative

specifications; column 1 shows estimates of the unconstrained equation, while the estimates in columns 2-4

set (respectively) J9 = 0, J11= 0, and J9 = J11 = 0. These constraints test whether allowing the effects of Time

and the number of producer to differ across markets of different sizes affects our results. Allowing these
28

effects to differ across markets does not seems to affect the estimates of the effects of any of variables on

rents, as the results are consistent across specifications. In all specifications, J1 is between 1.06 and 1.1,

indicating the elasticity of generic revenue with respect to branded revenue is fairly close to unity. In fact,

in no case can the null hypothesis that J1 = 1 be rejected at the 5% level. The sign of J2

 is negative, and the

sign of J3 is positive, suggesting that generic producers received less revenue in markets with more elastic

demand. As expected, the existence of competing brands before patent expiration also led to a statistically

significant reduction in generic revenue. As anticipated, generic revenues are lower in the post-scandal

period (J6< 0), and rises as the percentage of patients covered by traditional insurance falls (J7 < 0). As

anticipated, generic revenue increases over time, and therefore, the sign of J8 is negative. Finally,J10 is

positive, which suggests that demand elasticity is greater than 1.

The relationship between expected monthly revenue and time is depicted for three hypothetical drugs

in Figure 3. These drugs were chosen by dividing our sample of markets into three size categories on the

basis of pre-patent expiration sales, and analyzing the average drug in each category. These correspond to

monthly branded revenues prior to patent expiration of $2.77 million, $7.35 million and $19.46 million

respectively. The middle line shows the relationship for the average medium-sized market, and the upper

and lower lines depict the relationship for the average large and small markets, respectively. These depicted

relationships reflect the predicted increase in the number of generic producers over time (using equations

5 and 6), which in turn influences the total revenue. For all three hypothetical markets, monthly revenue

increases monotonically with time. In addition, in all three cases, the monthly revenue increases relatively

rapidly with time in the early portion of the sample, and tends to flatten out after about one year. 

D. ENTRY 

As noted in Section III, we are interested in explaining entry in two senses. First, we are interested

in the cross-sectional relationship between the total number of firms applying for ANDAs for each drug in

our sample and the available rents. Second, given the total number of applicants in each market, we are
29

(4')

interested in explaining the time series of entry; that is, how entry occurs over time for each drug. 

Equation (3) relates the number of generic producers that ultimately enter each market to the

available rents to generic entrants. The causality between available rents and the number of generic

producers runs in both directions, so that equations (4) and (5) must be estimated using an iterative procedure

where V adjusts to changes in :and 8. Using the Generic Spectra data, the structural form estimates that

result from this procedure are 

where the standard errors are in parentheses.

The model implies that during the non-scandal period the expected number of firms applying for

ANDAs increases by about 1.8 (= exp(0.59)) with every $1 million increase in the rents available in generic

production of the drug. The standard error of the estimate on the coefficient on the constant is 0.12, so that

we can be highly confident that the effect of a $1 million increase in V is to increase the number of ANDAs

by between 1.4 and 2.3 during the non-scandal period. The model also implies that the relationship was

weaker during the period following the generic drug scandal. This is consistent with newspaper accounts,

which describe the post-scandal period as one of greater FDA scrutiny of applications. Equation (4) implies

that the expected number of ANDAs increased by only about 0.94 (=exp(-.07)) with every $1 million

increase in V during this period. This suggests that the effect of increased scrutiny was substantial.

 Using these estimated coefficients, the model implies that for a market with a V of $5.9 million

(approximately the average market in our sample), : would be 7. 7 (evaluated at the mean value for

stringent). This in turn implies that the probability of exactly one application for an ANDA in this market

is about .5% (i.e., f(1) . .005, f(2) . .02, f(3) . .05, etc.).

The first two columns of Table 7 compare the expected number of approved ANDAs within three
 

25Scott Morton (2000) also examines the cross-sectional relationship between the number of entrants and market

characteristics. Her results are not directly comparable to ours because 65% of the drugs in her sample were low-revenue

drugs which had zero entry. Methodologically, her study differs from ours in that she estimates the reduced form

relationship between the number of entrants and characteristics, rather than jointly estimating the structural relationships

between rents and entry.

30

(5')

years of patent expiration to the actual number of approvals for each drug. For most of these drugs, this

procedure seems to yield an accurate prediction of the number of ANDAs. One conclusion from the analysis

is that incentive effects (as measured by the available rents) are important in determining the number of

applicants for ANDAs. It follows that factors that reduce the available rents can have a significant effect on

the number of applicants. 

We can also use this equation to estimate the costs of applying for an ANDA (including the

necessary testing). Specifically, the reciprocal on the coefficient on V from equation (4) gives the average

cost for an ANDA application. This implies that the average cost was slightly over $550,000 (= 1/1.8) during

the period prior to the scandal (the late 1980s), and was about $1.07 million (=1/0.94) million in the period

following the scandal.25 

The other sense in which we are interested in entry is explaining the time series of entry for each

drug. We model the probability of FDA approval in any given month with a hazard function, with the

probability of approval (8) related to the rents and regulatory environment, as

The positive coefficient on the size of the rents (V) indicates that V not only has an influence on the

total number of ANDAs (via equation (4)), but also suggests that higher V increases the probability that a

given firm gains FDA approval in a given month; each firm’s efforts to gain approval is apparently greater

in markets with higher available rents. Consistent with the premise of greater scrutiny during the postscandal period, the coefficient on the interactive Stringent term suggests that the probability of approval fell

during that period. 
31

Given our estimates of 8 and : from equations (4') and (5'), we calculate the probability of i firms

having gained approval by time t using the binomial formula (as shown in equation (6)). For example, this

implies that for a market with V = $5.9 million, the probabilities of i firms gaining approval in the first six

month following patent expiration are:

Months Since

Patent Expiration

Number of Generic Entrants

0 1 2 3 4 5+

1 51.1% 34.3% 11.5% 2.6% 0.4% 0.1%

2 27.7% 35.6% 22.8% 9.8% 3.1% 1.0%

3 15.8% 29.2% 26.9% 16.5% 7.6% 4.0%

4 9.5% 22.4% 26.3% 20.6% 12.1% 9.0%

5 6.0% 16.8% 23.7% 22.3% 15.7% 15.5%

6 3.9% 12.7% 20.5% 22.2% 18.0% 22.7%

These probabilities change over time, and by 24 months after expiration, the likelihood that 5 or more

applicants are approved in such a market is about 65%. Figure 4 depicts how the expected number of

approved firms varies over time for three different sized markets.

A graphical illustration of the reliability of this technique is presented in Figure 5. Figure 5 shows

the distribution of the predicted number of firms with ANDAs associated with each actual number of such

firms. For example, the display of points vertically above the “8" in Figure 5 is the set of values predicted

by our model for every observation in which the actual number of firms with ANDAs is eight. The predicted

values in this case tend to cluster around 8, although predictions range from approximately six to around

eleven. The squares in the figure reflect the actual and predicted number of total applications. Generally,

the distributions of predicted values follow the actual number fairly closely (i.e., the points tend to follow

the 45 degree line).

Another way of evaluating our estimates is to construct a “pseudo R2

,” which we create by taking

the difference between the actual and the expected number of entrants. For example, for each predicted value

associated with eight actual entrants, we take the residual (i.e., the difference between our estimate and

eight). We calculate the variance of this residual and of the actual number of entrants. These variances are
 

26 The calculation uses the coefficient estimates from the second column of Table 4 for the price effects of entry, and

the first column of Table 7 for the revenue effects.

32

akin to the unexplained sum of squares and the total sum of squares and their difference represents the

explained sum of the squares. The ratio of the explained sum to the total sum is similar to the usual R2

calculation. The ratio is 0.387 in this case. Given the large number of observations (over 1,000) and the fact

that our predictions use only five parameters, we view this as a reasonably accurate representation for such

a parsimonious model. As such, it suggests that the Poisson/hazard rate analysis is a useful way of modeling

the dynamic entry process. 

Given this path of expected entry, our estimates of the coefficients of equation (2) allow us to

calculate the expected margins at each point in time. The time paths of expected margins for typical largerevenue, medium-revenue and small-revenue drugs are depicted in Figure 6. As shown there, margins

decline faster for drugs with larger revenue, reflecting the fact that the predicted number of firms is larger

for those drug, and margins are inversely related to the number of firms. 

E. CALCULATION OF RENTS 

Equations 2, 6 and 7 relate the margins, expected number of generic producers and total revenue at

each point in time to observable characteristics of the drug markets. Observable characteristics include Subs,

Uses, Time, Frdrg and Branded Revenue. From these estimates, we can derive a relationship between the

expected total and per-firm rents in each market and these observable characteristics. 

The final 2 columns of Table 8 show the monthly pre-patent expiration revenues and the total rents

(V) to generic producers for each drug. The underlying calculation incorporates the estimates from equations

(2) - (7).26 As noted in Section III, while the calculations in Table 8 are made using the estimated

relationships from equations (4) and (5), the procedure used to derive these estimates was iterative so that

rents are treated as endogenous (i.e., in each iteration, the predicted value of rents from the previous iteration

is used to estimate the coefficients of equations (4) and (5)).
33

The relationship between expected monthly industry rents and time is depicted for three different

size markets in Figure 7. As in Figures 3, 4 and 6, the middle line shows the relationship for the average

medium-sized market (as measured in pre-expiration revenue) and the upper and lower lines depict the

average large and small market, respectively. Again, these relationships are drawn so that the time-varying

parameters are allowed to change. In particular, we allow the expected number of firms to increase over time

(using equation 6), which in turn influences both the total revenue and the margins. One interesting feature

of Figure 7 is that for all three hypothetical markets, the monthly rent increases with time in the early portion

of the sample period, but eventually reaches a maximum and then declines. The non-monotonicity of the

relationship reflects two offsetting effects. While total generic revenue increases over time, the margin

continues to fall with the increased competition. Since the increase in revenue is fairly slow after the first

year (see Figure 3), the effect of increased competition eventually dominates. 

VI. Using the Structural Estimates

The structural estimates detailed in Section V indicate how individual drug markets develop over

time. As discussed above, in the typical market in our sample our model predicts that the expected number

of firms applying for ANDAs in such a market would be 7.7, and given the predicted hazard rate, we obtain

the predicted path of entry illustrated in Figure 4. Using the estimates from Table 4, this in turn implies the

path of expected price illustrated in Figure 7. Given these relationships, we anticipate that the total expected

generic rents would be about $5.9 million. The structural equations can also be used to evaluate how the

number of firms will vary with the size of the market (as measured by pre-expiration revenues). Specifically,

if pre-expiration monthly revenues were $19.5 million, V would increase to about $9.2 million, and we would

expect about 11 firms would enter in equilibrium.

One interesting use of the structural estimates is simulating the effect of an exogenous change that

affects rents. Specifically, as discussed above, for several drugs whose patents expired in the mid 1990s, the
34

FDA approved multiple initial ANDAs. To the extent that this reflects a new policy, it will have effects in

both the short-run and the long-run. To see how such a policy can change the equilibrium, consider a drug

with $7.35 million in pre-patent expiration monthly revenues, and suppose the FDA announced that in the

future, they will approve the first four ANDAs on the same day (shortly after patent expiration), rather than

use the traditional path approval, which we modeled in equations (5) and (6). The direct effect of this is to

reduce the path of expected prices in the months following patent expiration. Holding the total number of

entrants constant, based on our estimates from equation (2) and our estimates of Dit, the expected price will

be 19% lower due to the new policy. 

There is also an indirect effect from this policy, which is to reduce the rents to gaining an ANDA.

Aggregate rents fall because the per-firm rents with 4 competing firms are less than 1/4 of the rents with a

single firm (and less than 1⁄2 the per-firm rents with two firms, etc). Hence, the most profitable outcomes are

precluded when this policy is in place. We calculate that if, in expectation, 7.7 firms continued to apply,

aggregate rents would decline to $4.5 million, so that if application costs are about $800,000 (as suggested

by the initial equilibrium), the 11 firms would, on average, lose money. In order for all entrants to earn nonnegative profits, the number of entrants would have to decline. We estimate that, conditional on four

(randomly-chosen) firms gaining ANDAs shortly after patent expiration, the resultant expected number of

entrants in equilibrium is slightly under six. Figure 8 shows the path of expected prices under the initial

equilibrium and this alternative equilibrium. Expected prices are much lower for the first 14 months under

the new policy (32% lower in the first month), but the effect of a smaller number of entrants eventually

dominates, and by the 15

th month price is actually higher with the new policy. On average, the effect of the

policy is to reduce price by about 1% over the first 3 years.

Another question that can be addressed by these estimates is the effect of the more stringent review

process that was instituted in the wake of the generic drug scandal. While the approach potentially screened

out some fraudulent drug applications, the change in the review process reduced the number of non-
35

fraudulent products approved as well. We can use our structural estimates to calculate the effect of the more

stringent standards on prices. In the median-sized market, our estimates indicate that the more stringent

review process reduced the expected number of applicants from 8.2 to 6.7, resulting in a reduction in

expected consumer surplus by about $2.5 million per market, or about 8.4% of revenue in that market. 

VII. Conclusion

This paper develops a methodology for estimating the structural relationships that describe generic

drug industry dynamics. These estimates enable us to describe how a market in this industry evolves from

monopoly pricing towards competitive pricing. Two elements of the methodology are noteworthy. First,

because the exact nature of the relationship between price and the number of competitors is critical to our

estimation, the structural assumptions made about this relationship will have a large influence on our results.

To minimize the possibility of misspecification, we allowed the data to determine the nature of the pricing

relationship by using a general functional form. Our interesting finding from this functional form is that the

negative effect of increased competition on prices continues until at least the fifth, and perhaps even the sixth

or seventh firm enters. 

The second noteworthy element of our estimation procedure is that we use a system of simultaneous

equations to estimate the relationship between entry and profitability. We do this because it is likely that the

causality between the number of entrants and the available rents runs in both directions. We estimate these

relationships simultaneously using functional form restrictions that follow from the economic model to

identify the system, which is then estimated using an iterative process.

Our estimates indicate that the flow of generic industry rents increases for the initial five to ten

months after patent expiration but then fall as more entrants compete away price-cost margins. We find that

more firms enter, and enter more quickly, in markets with greater expected rents. Finally, the size and time

paths of generic revenues, rents and the number of firms are greatly affected by measures reflecting the
36

expected market size. A consequence of these relationships is that the extent to which prices approach

competitive levels in a market depends upon, among other things, the potential revenues in the market. We

estimate that for markets of sufficient size (as measured by pre-patent expiration revenue), entry will

ultimately lead to near competitive pricing. In contrast, in small markets, prices will remain above marginal

cost without inducing additional entry. Finally, this analysis suggests that even in relatively large markets,

mergers between competitors can lead to higher prices. Moreover, such price increases may not induce entry,

even if potential entrants have the same entry costs as the incumbents, and entry would restore pre-merger

prices.
37

References

American Hospital Formulary Services (1996) Drug Information, (Bethesda, MD). 

Berndt, E., L. Bui, D. Reilly and G. Urban (1994) “The Roles of Marketing, Product Quality and Price

Competition in the Growth and Composition of the U.S. Anti-Ulcer Drug Industry NBER Working

Paper, # 4904.

Bond, R. and D. Lean (1977) Sales, Promotion and Product Differentiation in Two Prescription Markets,

Staff Report to the Federal Trade Commission, Washington, DC. 

Bresnahan, T. (1989) “Empirical Studies of Industries with Market Power” The Handbook of Industrial

Organization, pp. 

Bresnahan, T. and P. Reiss (1991) “Entry and Competition in Concentrated Markets” Journal of Political

Economy, 91, 977-109

Caves, R. M. Whinston and M. Hurwicz (1991) “Patent Expiration, Entry, and Competition in the U.S.

Pharmaceutical Industry,” Brookings Papers on Economic Activity: Microeconomics, 1-66.

Cook, A. (1998) How Increase Competition from Generic Drugs Has Affected Prices and Returns in the

Pharmaceutical Industry, Congressional Budget Office Study.

Clayman, C. ed. (1992 ) The American Medical Association Guide to Prescription and Over-the-Counter

Drugs (Chicago, IL). 

,Demsetz, H. (1973) “Industry Structure, Market Rivalry, and Public Policy,” Journal of Law and Economics,

16, 1-10.

DiMasi, J., R. Hansen, H. Grabowski and L. Lasagna (1991) “Cost of Innovation in the Pharmaceutical

Industry,” Journal of Health Economics, 10, 107-42.

Frank, R. and D. Salkever (1997) “Generic Entry and the Pricing of Pharmaceuticals,” Journal of Economics

and Management Strategy, 6, 75-90. 
38

Gisser, M. (1999) “Dynamic Gains and Static Losses in Oligopoly: Evidence from the Beer Industry”

Economic Inquiry, 37, pp. 554-75. 

Gort, M. and S. Klepper (1982) “Time Paths in the Diffusion of Product Innovations” The Economic

Journal, 92, pp. 630-53. 

Grabowski, H. and J. Vernon (1992) “Brand Loyalty, Entry, and Price Competition in Pharmaceuticals After

the 1984 Drug Act,” Journal of Law and Economics, 35, pp. 331- 51.

Grabowski, H. and J. Vernon (1994) “Returns to R&D on New Drug Introductions in the 1980s,” Journal

of Health Economics, 13, pp. 383-406.

Jovanovic, B. and G. MacDonald (1994) “The Life Cycle of a Competitive Industry,” Journal of Political

Economy, 102, pp. 322-47. 

Klepper, S. and K. Simons (2000) “The Making of an Oligopoly: Survival and Technological Change in the

Evolution of the U.S. Tire Industry” Journal of Political Economy, 108, pp. 728-60. 

Lu, Z. and W. Comanor (1998) “Strategic Pricing and New Pharmaceuticals,” Review of Economics and

Statistics, 80, 108-18.

Moulton, Brent R. (1986) “Random Group Effects and the Precision of Regression Estimates,” Journal of

Econometrics, 32, 385-97.

Office of Technology Assessment (1993) Pharmaceutical R&D: Costs, Risks and Rewards

Scott Morton, F. (2000) “Barriers to Entry, Brand Advertising, and Generic Entry in the U.S. Pharmaceutical

Industry,” International Journal of Industrial Organization, 18, 

Scott Morton, F. (1996) “Entry Decisions in the Generic Pharmaceutical Industry,” Rand Journal of

Economics, 30, 421-40.

Wiggins, S. and R. Maness (1996) “Price Competition in Pharmaceuticals: The Case of Anti-infectives”

Working Paper.
39

Table 1

Drugs and Dates of Generic Entry in the IMS Generic Spectra Data Set

Name Brand Generic Name Date of First Generic Entry

Alupent/Metaprel Metaproterenol January, 1988

Ascendin Amoxapine August, 1989

Ativan Lorazepam August, 1985

Atromid Clofibrate August, 1986

Blocadren Timolol Maleate May, 1989

Calan/Isoptin Verapamil April, 1986

Catapres Clonidine July, 1986

Cleocin Clindamycin October, 1987

Clinoril Sulindac April, 1990

Depakene Valproic Acid May, 1986

Desyrel Trazodone October, 1986

Duficef/Ultracef Cefadroxil March, 1989

Dyazide/Maxzide Triamterene September, 1987

Feldene Piroxicam April, 1992

Flexeril Cyclobenzaprine May, 1989

Haldol Haloperidol May, 1986

Inderal Propranolol July, 1985

Keflex Cephalexin April, 1987

Loniten Minoxidil March, 1987

Ludiomil Maprotiline January, 1988 

Minipress Prazosin May, 1989

Minocin Minocycline August, 1990

Moduretic Amilioride w/ HCTZ July, 1989

Nalfon Fenoprofen August, 1988

Procardia/Adalat Nifedipine September, 1990

Sinequan/Adapin Doxepin Apri, 1986

Tegretol Carbamezepine June, 1986

Tenormin Atenolol July, 1991

Tolectin Tolmetin December, 1991

Valium Diazepam August, 1985

Vancocin Vancomycin August, 1986

Ventolin Albuterol December, 1989

source: IMS Health, Generic SpectraTM
40

Table 2

Summary Statistics for IMS Generic Spectra Data

Variable Mean Standard

Deviation

Monthly Total Generic Revenue

($ millions)

2.28 2.82

Monthly Total Generic Quantity

(million Kg)

0.85 1.88

Monthly Branded Revenue Prior to

Patent Expiration ($ million)

11.18 10.34

Number of Substitutes Chemicals 9.70 5.77

Number of Uses 2.97 2.10

Multiple Brand Dummy 0.23 0.42

Fraction of Sales to Drugstores 0.89 0.17

Time trend 17.85 10.34

Number of ANDAs 5.82 4.30

Average Generic Wholesale Price 

relative to Pre-patent Branded Value

0.690 0.184

First ANDA Wholesale Price 

relative to Pre-patent Branded Value

0.710 0.198

Average Generic Retail Price 

relative to Pre-patent Branded Value

0.755 0.162

First ANDA Retail Price 

relative to Pre-patent Branded Value

0.779 0.165

Average ANDA Drugstore

Revenue/Quantity relative to Pre-patent

Branded Value

0.561 0.294

First ANDA Drugstore

Revenue/Quantity relative to Pre-patent

Branded Value

0.592 1.176

Average ANDA Hospital

Revenue/Quantity relative to Pre-patent

Branded Value

0.505 0.302

First ANDA Hospital

Revenue/Quantity relative to Pre-patent

Branded Value

0.568 0.351

source: IMS Health, Generic SpectraTM
41

Table 3

Summary Statistics for Data used in Price Regressions

Variable Mean Standard

Deviation

Pre-patent Revenue Growth Rate (% per month) 1.596 0.609

Time Trend 16.018 10.166

Number of Uses 3.715 2.396

Number of Substitute Chemicals 12.071 5.575

Multiple Brand Dummy 0.201 0.402

Dummy for 1 ANDA 0.055 0.228

Dummy for 2 ANDAs 0.091 0.289

Dummy for 3 ANDAs 0.098 0.298

Dummy for 4 ANDAs 0.116 0.321

Dummy for 5 ANDAs 0.098 0.298

Dummy for 6 ANDAs 0.091 0.289

Dummy for 7 ANDAs 0.067 0.251

Dummy for 8 ANDAs 0.067 0.251

Dummy for 9 ANDAs 0.061 0.240

Dummy for 10 ANDAs 0.043 0.203

Average Generic Wholesale Price 

relative to Pre-patent Branded Value

0.659 0.165

First ANDA Wholesale Price 

relative to Pre-patent Branded Value

0.686 0.182

Average Generic Retail Price 

relative to Pre-patent Branded Value

0.733 0.140

First ANDA Retail Price 

relative to Pre-patent Branded Value

0.762 0.137

Average ANDA Drugstore Revenue/Quantity

relative to Pre-patent Branded Value

0.483 0.244

First ANDA Drugstore Revenue/Quantity relative

to Pre-patent Branded Value

0.498 0.265

Average ANDA Hospital Revenue/Quantity relative

to Pre-patent Branded Value

0.505 0.251

First ANDA Hospital Revenue/Quantity relative to

Pre-patent Branded Value

0.525 0.285

 source: IMS Health, Generic SpectraTM
42

Table 4

Price Regression Results Using Sample of Pharmacy Prices: Dependent

 Variable is the Ratio of the Generic Price to Pre-Patent Expiration Branded Price

Average

Wholesale

Price

First

Wholesale

Price

Average

Retail Price

First Retail

Price 

Intercept 0.429*

(0.088)

0.296*

(0.102)

0.528*

(0.078)

0.531*

(0.080)

Time (times 10) 0.016

(0.014)

0.024

(0.016)

0.013

(0.012)

0.004

(0.010)

Number of Substitutes -0.003

(0.003)

0.005

(0.004)

-0.000 

(0.003)

0.005

(0.003)

Number of Uses 0.016+

(.007)

0.024+

(.008)

0.010

(0.006)

0.004

(.007)

Multiple 0.061

 (0.033)

0.014

 (0.035)

0.076*

(0.027)

0.076*

(0.028)

RGrowth 0.031

(0.022)

0.046

(0.025)

0.026

(0.019)

0.029

(0.020)

One Firm 0.287*

(0.066)

0.343*

(0.077)

0.225*

(0.059)

0.205*

(0.061)

Two Firms 0.229*

(0.056)

0.282*

(0.063)

0.166*

(0.049)

0.169*

(0.050)

Three Firms 0.257*

(0.052)

0.262*

(0.060)

0.205*

(0.046)

0.175*

(0.047)

Four Firms 0.195*

(0.047)

0.193*

(0.054)

0.150*

(0.042)

0.128*

(0.043)

Five Firms 0.180*

(0.049)

0.150*

(0.057)

0.138*

(0.044)

0.110*

(0.045)

Six Firms 0.113+

(0.048)

0.118+

(0.056)

0.079

(0.043)

0.065

(0.044)

Seven Firms 0.066

(0.056)

0.112

(0.065)

0.033

(0.050)

0.061

(0.051)

Eight Firms 0.063

(0.053)

0.087

(0.062)

0.035

(0.047)

0.065

(0.049)

Nine Firms 0.075

(0.053)

0.073

(0.062)

0.084

(0.047)

0.073

(0.049)

Ten Firms -0.002

(0.061)

-0.035

(0.070)

-0.007

(0.054)

-0.017

(0.056)

Adjusted R2

.242 .160 .178 .104

Number of Obs. 164 164 164 164

Asterisks denote significance at the 1% level and plus signs denote significance at

the 5% level. Standard errors in parentheses. 

 source: IMS Health, Generic SpectraTM
43

Table 5

Price Regression Results Using Shipment Data: Dependent

 Variable is the Ratio of Generic Price to Pre-Patent Expiration Branded Price

Average

Drugstore

price

First

Drugstore

Price

Average

Hospital

Price

First

Hospital

Price

Intercept 0.048

(0.106)

-0.046

(0.121)

0.354*

(0.130)

0.195*

(0.131)

Time (times 10) 0.029

(0.017)

0.036

(0.019)

-0.035 

(0.020)

-0.023 

(0.022)

Number of Substitutes 0.008+

(0.004)

0.009+

(0.004)

-0.012+ 

(0.005)

-0.015*

(0.005)

Number of Uses -0.000

(0.010)

-0.000

(0.010)

0.030*

(0.011)

0.055*

(0.011)

Multiple 0.069+

(0.034)

0.080+

(0.040)

0.025*

(0.045)

0.055

(0.045)

RGrowth -0.001

(0.026)

-0.001

(0.029)

0.011

(0.033)

0.048

(0.032)

One Firm 0.506*

(0.080)

0.582*

(0.090)

0.365*

(0.101)

0.464*

(0.104)

Two Firms 0.563*

(0.067)

0.645*

(0.076)

0.408*

(0.085)

0.491*

(0.085)

Three Firms 0.485*

(0.062)

0.555*

(0.072)

0.427*

(0.077)

0.490*

(0.078)

Four Firms 0.430*

(0.057)

0.507*

(0.065)

0.342*

(0.069)

0.420*

(0.070)

Five Firms 0.339*

(0.059)

0.448*

(0.070)

0.327*

(0.072)

0.391*

(0.076)

Six Firms 0.258*

(0.058)

0.328*

(0.066)

0.228*

(0.071)

0.312*

(0.073)

Seven Firms 0.225*

(0.067)

0.310*

(0.076)

0.150*

(0.081)

0.193+

(0.082)

Eight Firms 0.154+

(0.063)

0.225*

(0.063)

0.127*

(0.078)

0.184+

(0.078)

Nine Firms 0.141+

(0.064)

0.204*

(0.074)

0.155*

(0.077)

0.162+

(0.077)

Ten Firms 0.069

(0.073)

0.083

(0.088)

-0.031*

(0.089)

0.030

(0.092

Adjusted R2

.475 .459 .429 .507

Number of Obs. 164 156 158 140

Asterisks denote significance at the 1% level and plus signs denote significance at

the 5% level. Standard errors in parentheses. 

 source: IMS Health, Generic SpectraTM
44

Table 6

Regressions Results: Dependent Variable is the 

Natural Logarithm of Total Generic Revenue 

Model 1 Model 2 Model 3 Model 4

Intercept (J0) -1.359+

(0.548)

-1.354+

(0.549)

-1.327+

(0.549)

-1.313+

(0.550)

Log Pre-Expiration

Branded Revenues (J1)

1.087*

(0.039)

1.098*

(0.054)

1.062*

(0.044)

1.080*

(0.055)

Number of Substitutes

(J2

) 

-0.069*

(0.009)

-0.069*

(0.009)

-0.069*

(0.009)

-0.068*

(0.009)

Number of Uses (J3) 0.186*

(0.022)

0.186*

(0.022)

0.185*

(0.022)

0.185*

(0.022)

Fraction of Sales in Drug

Stores (J4) 

0.568*

(.218)

0.559*

(.220)

0.567*

(.218)

0.548*

(.220)

Multiple brands (J5

) -0.627*

(0.088)

-0.627*

(0.088)

-0.627*

(0.088)

-0.625*

(0.088)

Post-Scandal (J6) -0.569*

(0.140)

-0.571*

(0.140)

-0.568*

(0.140)

-0.573*

(0.140)

Fraction Conventional

Insurance (J7

)

-1.113

(0.777)

-1.115

(0.785)

-1.082

(0.777)

-1.142

(0.778)

Inverse of Time (J8) -3.211*

(0.212)

-3.204*

(0.214)

-3.649*

(0.447)

-3.682*

(0.451)

Log Pre-Patent

Revenues/Time (J9)

0.227

(0.205)

0.253

(0.210)

Inverse of the Number of

Firms (J10)

0.644*

(0.141)

0.702*

(0.253)

0.640*

(0.141)

0.756*

(0.256)

Log Pre-Patent

Revenues/Firms (J11)

-0.033

(0.119)

-0.066

(0.122)

Adjusted R2

.550 .550 .551 .550

Number of Obs. 1019 1019 1019 1019

Asterisks denote significance at the 1% level and plus signs denote significance at the 5%

level. Standard errors are in parentheses.

 source: IMS Health, Generic SpectraTM
45

Table 7

Predicted Number of Generic Producers and Generic Rents

Brand Name Generic Name Actual Number

of ANDAs

Predicted

Number of

ANDAs

Brand Sales

Prior to Patent

Expiration

($ millions)

Predicted Total

Generic Rents

($ millions)

Alupent/Metaprel Metaproterenol 9 5.82 8.56 4.84

Asendin Amoxapine 2 1.31 2.00 1.40

Ativan Lorazepam 13 11.61 11.37 6.44

Atromid-S Clofibrate 3 4.44 1.31 2.51

Blocadren Timolol 6 0.60 1.10 0.62

Calan/Isoptin Verapamil 10 9.68 8.06 5.37

Catapres Clonidine 13 9.52 5.33 5.28

Cleocin Clindamycin 1 2.72 1.10 1.97

Clinoril Sulindac 6 8.73 16.46 9.36

Depakene Valproic Acid 4 5.69 2.92 3.16

Desyrel Trazodone 9 7.82 5.56 4.58

Duricef/Ultracef Cefadroxil 3 6.50 10.99 6.47

Dyazide/Maxzide Triamterene/HCTZ 6 12.78 23.99 9.16

Feldene Piroxicam 9 12.29 27.37 13.16

Flexeril Cyclobenzaprine 4 7.48 10.97 7.72

Haldol Haloperiodol 17 12.02 7.09 6.66

Inderal Propranolol 18 17.69 24.66 9.80

Keflex Cephalexin 11 19.13 23.32 12.50

Loniten Minoxidil 5 5.93 2.77 3.80

Ludiomil Maprotiline 4 2.32 1.71 1.79

Minipress Prazosin 7 8.52 10.45 8.80

Minocin Minocycline 3 7.03 7.47 7.53

Moduretic Amiloride/HCTZ 6 5.07 4.70 5.43

Nalfon/Nalfon200 Fenoprofen 15 6.61 5.80 5.79

Procardia/Adalat Nifedipine 5 8.46 25.93 9.06

Sinequan/Adapin Doxepin 11 6.99 5.04 3.87

Tegretol Carbamazepine 6 9.91 5.57 5.49

Tenormin Atenolol 12 12.95 45.66 13.87

Tolectin Tolmetin 7 4.87 4.87 5.22

Valium Diazepam 16 14.81 28.07 8.21

Vancocin Vancomycin 7 11.94 9.45 6.61

Ventolin/Proventil Albuterol 14 9.22 36.59 9.87

source: IMS Health, Generic SpectraTM
39

Table 1

Drugs and Dates of Generic Entry in the IMS Generic Spectra Data Set

Name Brand Generic Name Date of First Generic Entry

Alupent/Metaprel Metaproterenol January, 1988

Ascendin Amoxapine August, 1989

Ativan Lorazepam August, 1985

Atromid Clofibrate August, 1986

Blocadren Timolol Maleate May, 1989

Calan/Isoptin Verapamil April, 1986

Catapres Clonidine July, 1986

Cleocin Clindamycin October, 1987

Clinoril Sulindac April, 1990

Depakene Valproic Acid May, 1986

Desyrel Trazodone October, 1986

Duficef/Ultracef Cefadroxil March, 1989

Dyazide/Maxzide Triamterene September, 1987

Feldene Piroxicam April, 1992

Flexeril Cyclobenzaprine May, 1989

Haldol Haloperidol May, 1986

Inderal Propranolol July, 1985

Keflex Cephalexin April, 1987

Loniten Minoxidil March, 1987

Ludiomil Maprotiline January, 1988 

Minipress Prazosin May, 1989

Minocin Minocycline August, 1990

Moduretic Amilioride w/ HCTZ July, 1989

Nalfon Fenoprofen August, 1988

Procardia/Adalat Nifedipine September, 1990

Sinequan/Adapin Doxepin Apri, 1986

Tegretol Carbamezepine June, 1986

Tenormin Atenolol July, 1991

Tolectin Tolmetin December, 1991

Valium Diazepam August, 1985

Vancocin Vancomycin August, 1986

Ventolin Albuterol December, 1989

data source: IMS Health, Generic SpectraTM
40

Table 2

Summary Statistics for IMS Generic Spectra Data

Variable Mean Standard

Deviation

Monthly Total Generic Revenue

($ millions)

2.28 2.82

Monthly Total Generic Quantity

(million Kg)

0.85 1.88

Monthly Branded Revenue Prior to

Patent Expiration ($ million)

11.18 10.34

Number of Substitutes Chemicals 9.70 5.77

Number of Uses 2.97 2.10

Multiple Brand Dummy 0.23 0.42

Fraction of Sales to Drugstores 0.89 0.17

Time trend 17.85 10.34

Number of ANDAs 5.82 4.30

Average Generic Wholesale Price 

relative to Pre-patent Branded Value

0.690 0.184

First ANDA Wholesale Price 

relative to Pre-patent Branded Value

0.710 0.198

Average Generic Retail Price 

relative to Pre-patent Branded Value

0.755 0.162

First ANDA Retail Price 

relative to Pre-patent Branded Value

0.779 0.165

Average ANDA Drugstore

Revenue/Quantity relative to Pre-patent

Branded Value

0.561 0.294

First ANDA Drugstore

Revenue/Quantity relative to Pre-patent

Branded Value

0.592 1.176

Average ANDA Hospital

Revenue/Quantity relative to Pre-patent

Branded Value

0.505 0.302

First ANDA Hospital

Revenue/Quantity relative to Pre-patent

Branded Value

0.568 0.351

data source: IMS Health, Generic SpectraTM
41

Table 3

Summary Statistics for Data used in Price Regressions

Variable Mean Standard

Deviation

Pre-patent Revenue Growth Rate (% per month) 1.596 0.609

Time Trend 16.018 10.166

Number of Uses 3.715 2.396

Number of Substitute Chemicals 12.071 5.575

Multiple Brand Dummy 0.201 0.402

Dummy for 1 ANDA 0.055 0.228

Dummy for 2 ANDAs 0.091 0.289

Dummy for 3 ANDAs 0.098 0.298

Dummy for 4 ANDAs 0.116 0.321

Dummy for 5 ANDAs 0.098 0.298

Dummy for 6 ANDAs 0.091 0.289

Dummy for 7 ANDAs 0.067 0.251

Dummy for 8 ANDAs 0.067 0.251

Dummy for 9 ANDAs 0.061 0.240

Dummy for 10 ANDAs 0.043 0.203

Average Generic Wholesale Price 

relative to Pre-patent Branded Value

0.659 0.165

First ANDA Wholesale Price 

relative to Pre-patent Branded Value

0.686 0.182

Average Generic Retail Price 

relative to Pre-patent Branded Value

0.733 0.140

First ANDA Retail Price 

relative to Pre-patent Branded Value

0.762 0.137

Average ANDA Drugstore Revenue/Quantity

relative to Pre-patent Branded Value

0.483 0.244

First ANDA Drugstore Revenue/Quantity relative

to Pre-patent Branded Value

0.498 0.265

Average ANDA Hospital Revenue/Quantity relative

to Pre-patent Branded Value

0.505 0.251

First ANDA Hospital Revenue/Quantity relative to

Pre-patent Branded Value

0.525 0.285

data source: IMS Health, Generic SpectraTM
42

Table 4

Price Regression Results Using Generic Spectra Pharmacy Data: Dependent

 Variable is the Ratio of the Generic Price to Pre-Patent Expiration Branded Price

Average

Wholesale

Price

First

Wholesale

Price

Average

Retail Price

First Retail

Price 

Intercept 0.429*

(0.088)

0.296*

(0.102)

0.528*

(0.078)

0.531*

(0.080)

Time (times 10) 0.016

(0.014)

0.024

(0.016)

0.013

(0.012)

0.004

(0.010)

Number of Substitutes -0.003

(0.003)

0.005

(0.004)

-0.000 

(0.003)

0.005

(0.003)

Number of Uses 0.016+

(.007)

0.024+

(.008)

0.010

(0.006)

0.004

(.007)

Multiple 0.061

 (0.033)

0.014

 (0.035)

0.076*

(0.027)

0.076*

(0.028)

RGrowth 0.031

(0.022)

0.046

(0.025)

0.026

(0.019)

0.029

(0.020)

One Firm 0.287*

(0.066)

0.343*

(0.077)

0.225*

(0.059)

0.205*

(0.061)

Two Firms 0.229*

(0.056)

0.282*

(0.063)

0.166*

(0.049)

0.169*

(0.050)

Three Firms 0.257*

(0.052)

0.262*

(0.060)

0.205*

(0.046)

0.175*

(0.047)

Four Firms 0.195*

(0.047)

0.193*

(0.054)

0.150*

(0.042)

0.128*

(0.043)

Five Firms 0.180*

(0.049)

0.150*

(0.057)

0.138*

(0.044)

0.110*

(0.045)

Six Firms 0.113+

(0.048)

0.118+

(0.056)

0.079

(0.043)

0.065

(0.044)

Seven Firms 0.066

(0.056)

0.112

(0.065)

0.033

(0.050)

0.061

(0.051)

Eight Firms 0.063

(0.053)

0.087

(0.062)

0.035

(0.047)

0.065

(0.049)

Nine Firms 0.075

(0.053)

0.073

(0.062)

0.084

(0.047)

0.073

(0.049)

Ten Firms -0.002

(0.061)

-0.035

(0.070)

-0.007

(0.054)

-0.017

(0.056)

Adjusted R2

.242 .160 .178 .104

Number of Obs. 164 164 164 164

Asterisks denote significance at the 1% level and plus signs denote significance at

the 5% level. Standard errors in parentheses. 

data source: IMS Health, Generic SpectraTM
43

Table 5

Price Regression Results Using Generic Spectra Pharmacy Data: Dependent

 Variable is the Ratio of Generic Price to Pre-Patent Expiration Branded Price

Average

Drugstore

price

First

Drugstore

Price

Average

Hospital

Price

First

Hospital

Price

Intercept 0.048

(0.106)

-0.046

(0.121)

0.354*

(0.130)

0.195*

(0.131)

Time (times 10) 0.029

(0.017)

0.036

(0.019)

-0.035 

(0.020)

-0.023 

(0.022)

Number of Substitutes 0.008+

(0.004)

0.009+

(0.004)

-0.012+ 

(0.005)

-0.015*

(0.005)

Number of Uses -0.000

(0.010)

-0.000

(0.010)

0.030*

(0.011)

0.055*

(0.011)

Multiple 0.069+

(0.034)

0.080+

(0.040)

0.025*

(0.045)

0.055

(0.045)

RGrowth -0.001

(0.026)

-0.001

(0.029)

0.011

(0.033)

0.048

(0.032)

One Firm 0.506*

(0.080)

0.582*

(0.090)

0.365*

(0.101)

0.464*

(0.104)

Two Firms 0.563*

(0.067)

0.645*

(0.076)

0.408*

(0.085)

0.491*

(0.085)

Three Firms 0.485*

(0.062)

0.555*

(0.072)

0.427*

(0.077)

0.490*

(0.078)

Four Firms 0.430*

(0.057)

0.507*

(0.065)

0.342*

(0.069)

0.420*

(0.070)

Five Firms 0.339*

(0.059)

0.448*

(0.070)

0.327*

(0.072)

0.391*

(0.076)

Six Firms 0.258*

(0.058)

0.328*

(0.066)

0.228*

(0.071)

0.312*

(0.073)

Seven Firms 0.225*

(0.067)

0.310*

(0.076)

0.150*

(0.081)

0.193+

(0.082)

Eight Firms 0.154+

(0.063)

0.225*

(0.063)

0.127*

(0.078)

0.184+

(0.078)

Nine Firms 0.141+

(0.064)

0.204*

(0.074)

0.155*

(0.077)

0.162+

(0.077)

Ten Firms 0.069

(0.073)

0.083

(0.088)

-0.031*

(0.089)

0.030

(0.092

Adjusted R2

.475 .459 .429 .507

Number of Obs. 164 156 158 140

Asterisks denote significance at the 1% level and plus signs denote significance at

the 5% level. Standard errors in parentheses. 

data source: IMS Health, Generic SpectraTM
44

Table 6

Regressions Results: Dependent Variable is the 

Natural Logarithm of Total Generic Revenue 

Model 1 Model 2 Model 3 Model 4

Intercept ( 0) -1.359+

(0.548)

-1.354+

(0.549)

-1.327+

(0.549)

-1.313+

(0.550)

Log Pre-Expiration

Branded Revenues ( 1)

1.087*

(0.039)

1.098*

(0.054)

1.062*

(0.044)

1.080*

(0.055)

Number of Substitutes

( 2) 

-0.069*

(0.009)

-0.069*

(0.009)

-0.069*

(0.009)

-0.068*

(0.009)

Number of Uses ( 3) 0.186*

(0.022)

0.186*

(0.022)

0.185*

(0.022)

0.185*

(0.022)

Fraction of Sales in Drug

Stores ( 4

) 

0.568*

(.218)

0.559*

(.220)

0.567*

(.218)

0.548*

(.220)

Multiple brands ( 5

) -0.627*

(0.088)

-0.627*

(0.088)

-0.627*

(0.088)

-0.625*

(0.088)

Post-Scandal ( 6) -0.569*

(0.140)

-0.571*

(0.140)

-0.568*

(0.140)

-0.573*

(0.140)

Fraction Conventional

Insurance ( 7)

-1.113

(0.777)

-1.115

(0.785)

-1.082

(0.777)

-1.142

(0.778)

Inverse of Time ( 8) -3.211*

(0.212)

-3.204*

(0.214)

-3.649*

(0.447)

-3.682*

(0.451)

Log Pre-Patent

Revenues/Time ( 9

)

0.227

(0.205)

0.253

(0.210)

Inverse of the Number of

Firms ( 10)

0.644*

(0.141)

0.702*

(0.253)

0.640*

(0.141)

0.756*

(0.256)

Log Pre-Patent

Revenues/Firms ( 11)

-0.033

(0.119)

-0.066

(0.122)

Adjusted R2.550 .550 .551 .550

Number of Obs. 1019 1019 1019 1019

Asterisks denote significance at the 1% level and plus signs denote significance at the 5%

level. Standard errors are in parentheses.

data source: IMS Health, Generic SpectraTM
45

Table 7

Predicted Number of Generic Producers and Generic Rents

Brand Name Generic Name Actual Number

of ANDAs

Predicted

Number of

ANDAs

Brand Sales

Prior to Patent

Expiration

($ millions)

Predicted Total

Generic Rents

($ millions)

Alupent/Metaprel Metaproterenol 9 5.82 8.56 4.84

Asendin Amoxapine 2 1.31 2.00 1.40

Ativan Lorazepam 13 11.61 11.37 6.44

Atromid-S Clofibrate 3 4.44 1.31 2.51

Blocadren Timolol 6 0.60 1.10 0.62

Calan/Isoptin Verapamil 10 9.68 8.06 5.37

Catapres Clonidine 13 9.52 5.33 5.28

Cleocin Clindamycin 1 2.72 1.10 1.97

Clinoril Sulindac 6 8.73 16.46 9.36

Depakene Valproic Acid 4 5.69 2.92 3.16

Desyrel Trazodone 9 7.82 5.56 4.58

Duricef/Ultracef Cefadroxil 3 6.50 10.99 6.47

Dyazide/Maxzide Triamterene/HCTZ 6 12.78 23.99 9.16

Feldene Piroxicam 9 12.29 27.37 13.16

Flexeril Cyclobenzaprine 4 7.48 10.97 7.72

Haldol Haloperiodol 17 12.02 7.09 6.66

Inderal Propranolol 18 17.69 24.66 9.80

Keflex Cephalexin 11 19.13 23.32 12.50

Loniten Minoxidil 5 5.93 2.77 3.80

Ludiomil Maprotiline 4 2.32 1.71 1.79

Minipress Prazosin 7 8.52 10.45 8.80

Minocin Minocycline 3 7.03 7.47 7.53

Moduretic Amiloride/HCTZ 6 5.07 4.70 5.43

Nalfon/Nalfon200 Fenoprofen 15 6.61 5.80 5.79

Procardia/Adalat Nifedipine 5 8.46 25.93 9.06

Sinequan/Adapin Doxepin 11 6.99 5.04 3.87

Tegretol Carbamazepine 6 9.91 5.57 5.49

Tenormin Atenolol 12 12.95 45.66 13.87

Tolectin Tolmetin 7 4.87 4.87 5.22

Valium Diazepam 16 14.81 28.07 8.21

Vancocin Vancomycin 7 11.94 9.45 6.61

Ventolin/Proventil Albuterol 14 9.22 36.59 9.87

data source: IMS Health, Generic SpectraTM
46

Figure 1

Estimating Structural Relationships

Definitions

V - present value of total expected generic rents

n - total number of ANDAs (entrants)

Pit - total generic rents when there are i competing generic producers at time t

Pit - generic price when there are i competing generic producers at time t

Qit - total generic quantity when there are i competing generic producers at time t

rit - probability of i competing generic producers at time t

St - Survivorship probability at time t
47

Figure 2

Estimated Average Price-Cost Markup (and Twice the Standard Error)

As a Function of the Number of Firms

data source: IMS Health, Generic SpectraTM
48

Figure 3

Predicted Market Revenue

data source: IMS Health, Generic SpectraTM
49

Figure 4 

Predicted Number of Firms

data source: IMS Health, Generic SpectraTM
50

Figure 5

Generic Entry Goodness of Fit

data source: IMS Health, Generic SpectraTM

Boxes correspond to number of total applicants for ANDAs. Diamond shapes corresponds to the panel

of observations on the number of firms are different points in time.
51

Figure 6

Expected Margins

data source: IMS Health, Generic SpectraTM
52

Figure 7

Expected Industry Profits

data source: IMS Health, Generic SpectraTM
53

Figure 8

Margin Paths Over Time

data source: IMS Health, Generic SpectraTM