WORKING 

PAPERS 

The Effect of Generic Drug Competition on Generic Drug Prices During 

 the Hatch-Waxman 180-Day Exclusivity Period 

Luke M. Olson 

Brett W. Wendling 

WORKING PAPER NO. 317 

April 2013 

FTC Bureau of Economics working papers are preliminary materials circulated to stimulate discussion 

and critical comment. The analyses and conclusions set forth are those of the authors and do not 

necessarily reflect the views of other members of the Bureau of Economics, other Commission staff, or 

the Commission itself. Upon request, single copies of the paper will be provided. References in 

publications to FTC Bureau of Economics working papers by FTC economists (other than 

acknowledgment by a writer that he has access to such unpublished materials) should be cleared with the 

author to protect the tentative character of these papers. 

BUREAU OF ECONOMICS 

FEDERAL TRADE COMMISSION 

WASHINGTON, DC 20580
* Corresponding author. E-mail: bwendling@ftc.gov. Mailing address: 600 Pennsylvania Ave 

NW, Washington, DC 20580. We thank David Schmidt for invaluable advice throughout the 

process. We also thank Viola Chen, Abe Dunn, Daniel Hosken, Karen Goldman, Chris Metcalf, 

Elizabeth Schneirov, Steven Tenn and Nathan Wilson for providing helpful comments. Greg 

Kuczura provided outstanding research assistance. The views expressed in this paper are those of 

the authors and do not necessarily represent the views of the Federal Trade Commission or any 

individual Commissioner. 

Estimating the Effect of Entry on Generic Drug Prices 

Using Hatch-Waxman Exclusivity 

Luke M. Olson 

Brett W. Wendling 

Abstract: 

Generic drugs play an important role in disciplining drug prices and controlling rising drug costs. 

However, the effect that an additional generic drug competitor has on drug prices is difficult to 

measure because the number of firms competing in a market is endogenously determined. We 

identify the causal effects of a second and a third generic competitor on generic drug prices by 

exploiting the 180-day period of marketing exclusivity created by provisions of the HatchWaxman Act. The effects of the second and third competitors on price have important 

implications for drug competition policy and the interpretation of theory relating price to 

competition in generic drug markets. We find significant biases associated with estimates that do 

not properly account for endogenous entry. Specifically, we find that the failure to account for 

endogenous entry leads to significant underestimation of the effects of two and three competitors 

on generic drug prices, especially among large drugs. 

 Keywords: pharmaceutical, prescription drugs, generic entry, price competition 

JEL Classification Codes: L11, L13, L6
- 1 - 

I. Introduction 

The relationship between generic drug prices and competition is receiving increased 

attention from policy-makers concerned about rapidly growing medical care costs.1

 Many 

policy-makers view generic drug competition as the principal method to contain the rapid 

growth in drug costs, which currently represents the fastest growing segment of 

healthcare expenditures in the United States.2 In addition, numerous laws, regulations and 

legal precedents play an important role in directly affecting drug competition by altering 

the structure and competitive environment of these markets. For example, the HatchWaxman Act has been instrumental in shaping the competitive environment for both 

generic and branded drugs; the Supreme Court is currently considering the legality of 

some types of patent litigation settlements that affect competition between branded and 

generic competitors; and, Medicare Part D has led to a significant change in the provision 

of prescription drugs for a growing proportion of the population.3 These policies and 

precedents, alongside antitrust competition policy, underscore the importance of drug 

market competition in U.S. healthcare policy. 

 

1

 FTC 2011. See also http://www.ftc.gov/os/highlights/2012/topics/prescriptionDrugs.shtml, where the FTC 

specifically identifies drug prices in its policy mission, and, http://www.kaiseredu.org/IssueModules/Prescription-Drug-Costs/Policy-Research.aspx, which identifies various instruments to control 

drug costs. 

2

 Drug expenditures have increased by over 8.5% per year during the period 2001-2011; whereas overall 

health expenditures have increased by approximately 6.5% over the same period (see 

https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-andReports/NationalHealthExpendData/Downloads/tables.pdf ). See, also, the response to this growth made by 

the Secretary of Health and Human Services: http://aspe.hhs.gov/sp/reports/2010/genericdrugs/ib.pdf . 3

 The Supreme Court case is Federal Trade Commission v. Watson Pharmaceuticals, Inc., No. 12-416. The 

Hatch-Waxman Act is formally the “Drug Price Competition and Patent Term Restoration Act of 1984.” 

Medicare Part D, formally known as the “Voluntary Prescription Drug Benefit Program,” is a section of the 

Medicare Modernization Act, formally known as the “Medicare Prescription Drug, Improvement, and 

Modernization Act of 2003.” 
- 2 - 

In this study, we estimate the empirical relationship between generic drug prices and the 

number of generic drug competitors. Generic drugs appear to compete in homogeneous 

goods product markets since generic drugs invest little in marketing or other forms of 

differentiation, and are, by law, clinically equivalent to a reference drug. Many of the 

theoretical models of oligopolistic competition among homogeneous goods predict that 

the first few entrants in the market have larger effects on prices than later entrants.4

Empirically testing whether these predictions are true is important for policy-makers that 

either rely upon “generic competition” to discipline drug costs, or are tasked to evaluate 

the effects of generic drug competition. The results from these tests may help evaluate the 

effects of government policy, since some studies have shown that drug market regulation 

may have a deleterious effect on the potential benefits of competition (see e.g., Danzon 

and Chao 2000). 

Estimating the causal relationship between price and the number of competitors is 

difficult since entry decisions by generic competitors may be correlated with unobserved 

determinants of price. We address this potential endogeneity concern by exploiting the 

180-day marketing exclusivity period awarded to successful patent litigants under the 

provisions of the Hatch-Waxman Act.5

 During the exclusivity period, the FDA explicitly 

limits the set of generic competitors that can market the drug. The FDA considers 

regulatory characteristics, such as the presence of patents, as the basis for whether a 

 

4

 See Shapiro (1989) and Chapters 4 and 5 of Vives (2001) for more comprehensive reviews of the theory 

of oligopolistic competition in homogeneous product models. 

5

 Prescription drug markets are subject to various exclusivity periods that correspond to patent protection, 

Hatch-Waxman provisions, and other regulations. This paper focuses on the 180-day exclusivity period 

awarded to generic manufacturers that successfully litigate against branded patents under the provisions of 

the Hatch-Waxman Act. Unless otherwise noted, the “exclusivity” period refers to this 180-day period. 
- 3 - 

specific generic firm can market the drug during the exclusivity period. Since these 

regulatory characteristics are unlikely correlated with price, the number of competing 

firms during the exclusivity period is likely exogenous. 

We also relax the assumption of exogenous firm entry during the exclusivity period by 

using instrumental variables (IV). Our IV modeling strategy introduces an instrument that 

the prior literature has not used: whether the FDA has classified the drug formulation as a 

“new chemical entity” (NCE). Designation as an NCE drug by the FDA potentially 

facilitates competition between numerous firms during the exclusivity period by affecting 

the timing of generic approval filings. We demonstrate that the NCE designation is 

correlated with the number of generic competitors observed during the exclusivity period. 

Since the NCE designation only influences market structure through the timing of a 

regulatory filing, we argue that it is uncorrelated with unobserved determinants of price, 

and is thus a valid instrument. 

Our results suggest that controlling for endogenous entry is an important consideration in 

estimating the relationship between price and market structure in generic drug markets. 

Failure to control for endogenous entry can lead to significant biases associated with the 

effects that generic drug competition has on generic drug prices. Specifically, we find 

that an additional competitor lowers generic drug prices by a greater extent during the 

180-day exclusivity period than outside of it. For example, we find that, outside of the 

exclusivity period, three competitors lower wholesale generic prices by approximately 

32% relative to a single generic competitor. In contrast, during the exclusivity period we 
- 4 - 

find that three competitors lower generic drug prices by approximately 48% relative to a 

single generic competitor. We attribute the differences in these estimates to the biases 

associated with endogenous entry outside of the exclusivity period. In our analysis, we 

also divide the sample into drugs with higher than average revenue, “large drugs,” and 

those with lower than average revenue, “small drugs.” Although the differences between 

the marginal effect estimates during the exclusivity period and outside the exclusivity 

period are economically important in the full sample of drugs, they are particularly 

pronounced in the sample of large drugs. They are also statistically significant in that 

sample.6

Our instrumental variable results suggest that the first three generic competitors have an 

even larger effect on prices than our estimates that treat entry during exclusivity as 

exogenous. The IV estimates provide further evidence that endogenous entry results in 

underestimated effects of the first three competitors on generic drug prices. All of our 

results suggest that accounting for these biases leads to results that are more consistent 

with the predictions from the relevant oligopoly pricing models. 

The next sections provide a background of the theory and the literature (section II), our 

identification strategy (section III), a description of the data (section IV), and our 

estimation strategy and results (Section V). We conclude in Section VI. 

 

6

 The differences in the marginal effect estimates during the exlucisivity period and outside the exclusivity 

period are not statistically significant in the sample of small drugs. 
- 5 - 

II. Background 

Generic drugs are defined by a unique combination of ‘active ingredients,’ strength, and 

dosage forms. Federal law requires each generic drug manufacturer’s product to be 

bioequivalent to a reference drug specified in the approval application to the FDA, where 

the reference drug is usually the relevant strength and dosage form of the branded drug. 

Generic drugs are sold in auctions to retail pharmacies, and are rarely advertised, either to 

consumers or to physicians. The marketing practices of generic drugs often reflect almost 

no attempt at differentiation from other versions of the same product. Indeed, generic 

drug manufacturers usually market the drug under the name of the active ingredient, such 

that several generic drug producers market the product under the same name. For 

example, each of the producers of generic Prilosec markets its product simply as 

“omeprazole,” the active ingredient in the reference drug, Prilosec. 

Many oligopolistic competition models of homogeneous goods, including homogeneous 

product Cournot competition, predict steep declines in prices with only a few 

competitors. However, some drugs are observed with many actively marketing 

manufacturers (i.e., greater than ten). Moreover, some empirical studies of generic drug 

market competition find that generic drug prices continue to decline in the number of 

competitors well past four competitors. This result is consistent with some oligopoly 

models, including homogeneous Cournot competition models, but is inconsistent with 

others, such as homogeneous Bertrand under certain cost assumptions. 
- 6 - 

The early empirical studies of generic drug competition impose restrictive functional 

relationships between price and the number of manufacturers, such as linear or quadratic 

competitor terms. Estimates from these studies suggest that a large number of firms are 

necessary to achieve competitive prices (e.g., Caves et. al. 1991, Grabowski and Vernon 

1992, Frank and Salkever 1997, Wiggins and Maness 2004). For example, the coefficient 

estimates from Caves et al. (1991) predict that a drug market with 15 competitors has a 

predicted price that is less than half of that of a market with three competitors. 

More recent studies estimate flexible model specifications that allow for comparisons of 

differential marginal effects of specific competitors (i.e., dummy variables for each 

competitor). Reiffen and Ward (2005) find economically small and statistically 

insignificant differences between prices in markets with one generic firm and markets 

with fewer than four generic firms. Their estimates imply that later entrants (greater than 

three) cause larger price effects than earlier entrants (less than four). Regan (2008) 

employs a different drug sample to consider differential pricing patterns using a 

specification similar to that of Reiffen and Ward (2005). However, in constrast to Reiffen 

and Ward (2005), she finds that all generic competitors, other than the first one, have 

small pricing effects. She finds that no competitor, other than the first one, has an effect 

on generic drug prices that is statistically different from zero. These results are consistent 
- 7 - 

with Bertrand competition against the branded drug (i.e., only a single generic competitor 

is necessary to achieve marginal cost pricing).7

Resolving this conflicting evidence about the nature of generic drug competition has 

important implications for merger enforcement and cost containment policies. For 

example, a review of a recent generic drug firm merger provides insight into the policy 

decisions of the antitrust enforcement agencies.8 We observe that the Federal Trade 

Commission (FTC) required seven divestitures among products currently marketed by 

both parties in the recent Watson-Actavis merger. Each of the seven divestitures were 

among products with fewer than six actively marketing competitors prior to the merger. It 

follows from this observation that an effective merger policy relies upon accurate 

estimates of the relationship between price and the number of competitors, especially 

when the number of competitors is small. 

Our paper reconsiders the empirical relationship between competition and generic drug 

prices by exploiting the 180-day exclusivity provision of the Hatch-Waxman Act. We 

describe our identification strategy in the next section. 

 

7

 Regan (2008) offers a mildly different interpretation of some of her results. In her generic drug pricing 

equation, she suggests that the coefficient estimates on the dummy variables that represent the latest 

entrants (i.e., entrants 4-5, and 6+) “increase in magnitude suggesting that the degree of price competition 

increases with entry.” However, neither estimate is larger than 1% relative to a single generic competitor, 

and both are statistically insignificant. 

8 http://www.ftc.gov/opa/2012/10/watson.shtm
- 8 - 

III. FDA Regulation and Model Identification 

In order to estimate the causal relationship between generic drug prices and the number 

of marketing competitors, the empirical strategy must control for market characteristics 

correlated with both the number of firms and price. However, unobserved factors that 

attract entry, such as unobserved determinants of either demand or costs, may also help to 

explain price. Failing to control for these unobserved market characteristics could bias 

estimates of the effects of competition on price. Figure 1 illustrates the importance of this 

issue in the market for generic drugs. This figure considers the relationship between price 

and competition for two sets of drugs with different entry profiles. The solid line 

represents drugs that are never observed with more than four manufacturers. The dotted 

line represents drugs eventually observed with at least five competing generic 

manufacturers. We are agnostic about what explains the differences in whether these 

drugs are attractive to enter, although cost and demand conditions are likely important. 

Each point in the figure represents the average relative generic price, indexed against the 

average price facing a single manufacturer for markets facing two, three, and four 

competitors.9

The two sets of drugs have very different pricing profiles. Drugs that eventually attract at 

least five competitors face a steep decline in prices with respect to the number of 

competitors considered in the Figure. In contrast, drugs that do not attract more than four 

 

9

 Figure 1 reflects information contained in the first 24 months of generic competition. The relative price is 

the ratio of generic drug prices at time t to brand prices in the quarter prior to generic entry. See section IV 

for a more in-depth discussion of how these prices are constructed. 
- 9 - 

manufacturers during our sample have a shallow pricing profile. These profiles are 

interesting since, together, they can generate the pricing patterns found in some of the 

previous literature of generic drug competition. Drugs that attract five or more 

competitors are rarely observed with fewer than five entrants, and drugs that do not 

attract more than four entrants are never observed with more than four entrants.10 Thus, a 

regression pooling both sets of drugs would falsely create the appearance that the effects 

of the first few competitors are small (i.e., the shallower slope), and then the effect jumps 

discretely between four and six competitors (i.e., from the shallow sloped line to the steep 

sloped line).11

Our formal empirical model attempts to control for this type of potentially endogenous 

selection by exploiting terms and provisions of the Hatch-Waxman Act. The HatchWaxman Act currently governs FDA marketing approval requirements for generic drugs, 

and has done so since 1984. The Act establishes the terms and requirements of the 

Abbreviated New Drug Application (ANDA) process, a process which, in 1984, 

significantly reduced the costs of gaining generic marketing approval.12 In addition, the 

Act also requires ANDA applicants to certify that it will not violate patents listed in the 

 

10Among drugs eventually attracting more than five entrants, 26% of months are observed with fewer than 

five manufacturers, including months during exclusivity. Excluding the exclusivity period, this figure falls 

to 16%. Overall, drugs that eventually attract more than five manufacturers account for less than 18% of 

months with fewer than five manufacturers. 

11 Reiffen and Ward (2005) find results that are consistent with these types of discrete jumps in markets 

with greater than 4 competitors. 

12 Conditional upon demonstrable bioequivalence to a reference drug, the ANDA process allows generic 

drug applicants to rely upon clinical trial evidence provided in the original New Drug Application (NDA). 

Prior to the Hatch-Waxman Act, the generic filer was required to conduct its own clinical trials. Due to the 

expense of performing clinical trials, generic firms have rarely entered without an ANDA submission since 

passage of the Act. 
- 10 - 

FDA’s Orange Book.13 However, in order to encourage generic firms to pursue entry 

prior to expiration of claimed patent protection of questionable patents. To do so, a 

generic drug manufacturer must first submit to the FDA a “Paragraph Four” ANDA in 

which it certifies that (a) its generic drug will not infringe patents listed in the FDA’s 

“Orange Book,” and/or that (b) the relevant Orange Book patents are invalid.14 The 

applicant must also provide notice of its certification to the NDA filer, and any other 

patent holders. The Paragraph Four application must also include a detailed statement 

that explains why the applicant believes the patent is invalid or will not be infringed. 

Paragraph Four applicants that are designated by the FDA as the first applicant to file a 

sufficiently complete application are eligible to win an award of 180-days exclusivity, 

conditional upon a successful patent challenge. 

Our identification strategy relies upon the terms and conditions associated with the 180-

day exclusivity period. During the exclusivity period, the FDA will not grant marketing 

approval to any ANDA other than a ‘first-filer.’ This feature implies that all drugs are 

observed with a limited number of firms competing during the exclusivity period, 

regardless of whether the drug eventually attracts numerous competitors. However, under 

some circumstances, the FDA allows multiple generic competitors to market during 

exclusivity. For example, the brand firm may market an “authorized” generic (AG) under 

 

13 The length and terms of the marketing restrictions associated with the Hatch-Waxman Act often depend 

upon the approval conditions of the associated reference drug, such as whether the FDA designated the 

drug as a new chemical entity (NCE). For example, all New Drug Applications approved by the FDA are 

subject to at least a 3-year marketing “exclusivity” period. The associated exclusivity is 5 years for a drug 

designated as a NCE. In addition, the FDA does not accept ANDA filings prior to 4 years following the 

introduction of a NCE. See 

http://www.fda.gov/Drugs/DevelopmentApprovalProcess/SmallBusinessAssistance/ucm069962.htm for 

more information. 

14 Paragraph IV refers to the relevant provision of the Hatch-Waxman Act. 
- 11 - 

the authority of the branded product’s New Drug Application. The FDA may also 

designate more than one applicant as a “first-filer” if multiple applicants file on the same 

day.15

The ANDA approval process is sufficiently complex such that any applicant, 

unencumbered by other regulations, would be unlikely to file a sufficiently complete 

ANDA on exactly the same day as any other applicant. However, if the FDA designates 

the reference drug as an NCE, then the FDA imposes an extended filing prohibition that 

increases the likelihood that multiple filers file an ANDA on the same day (i.e., the day 

the prohibition expires), and are therefore designated as “first filers” by the FDA. The 

regulation prohibits potential generic applicants from submitting an ANDA application 

until 4 years following the introduction of an NCE brand drug.16 This extended filing 

prohibition, which is only associated with drugs designated as an NCE, is often enough 

time for a firm to substantially complete an ANDA application. 

We exploit these features of the Hatch-Waxman Act to identify the effect of competition 

on price. We begin by estimating the effect of competition on prices during the 180-day 

exclusivity period. We treat the exclusivity as a period of exogenous entry since a drug 

that might otherwise attract many entrants only competes with a few competitors during 

 

15 See “Guidance for Industry: 180-Day Exclusivity When Multiple ANDAs Are Submitted on the Same 

Day,” for a detailed discussion of multiple first filers. 

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm072851.

pdf 16 A 5-year period of exclusivity is granted to new drug applications for products containing chemical 

entities never previously approved by FDA either alone or in combination. Applications may be submitted 

after 4 years if they contain a certification of patent invalidity or noninfringement. (Accessed February 28, 

2013) http://www.fda.gov/Drugs/DevelopmentApprovalProcess/SmallBusinessAssistance/ucm069962.htm 
- 12 - 

exclusivity. We show how endogenous entry may bias the estimates of competition by 

comparing the estimated effects of competition during exclusivity to the estimated effects 

outside of it, where unobserved determinants of entry are uncontrolled. 

Next, we relax the assumption that entry during the exclusivity period is exogenous. We 

use instrumental variable (IV) techniques to model the number of competitors during 

exclusivity as a function of the NCE designation and the annualized pre-entry market size 

of the drug (measured in dollars). Our IV strategy treats both the NCE designation and 

the pre-entry market size of the drug as valid instruments. As described above, drugs 

classified as NCEs are associated with lengthy regulatory prohibitions that increase the 

likelihood of multiple first-filers. However, the FDA, an independent government 

agency, designates a drug as an NCE years in advance of generic drug competition. 

Consequently, designation of the branded drug as an NCE is unlikely correlated with 

generic pricing. 

The previous literature has extensively used market size to instrument for the number of 

generic competitors since the size of a drug has no obvious theoretical relationship with 

price (e.g., Caves et al. 1991, Frank and Salkever 1997, and Regan 2008). Many of the 

arguments for its use as a valid instrument in the previous literature extend to the 

exclusivity period. In addition, branded drugs are more likely to introduce an AG into 

large drug markets (FTC 2011), and larger drugs attract more ANDA filings (ScottMorton 1999). However, the entry restrictions of the exclusivity period may mute some 

of the power that market size has in explaining the number of competitors. 
- 13 - 

IV. Data 

Our analysis employs monthly wholesale data from IMS Health (IMS).17 IMS reports 

unit and dollar sales information separately for every prescription drug sold in the United 

States. The sample considers oral solid medications sold during April 2003 through 

December 2010.18 We define a drug to be the combination of molecule (i.e., active 

ingredient), dosage form, strength, and therapeutic class. For example, the 10 mg and 20 

mg tablet formulations of benazepril, an ACE inhibitor, are two distinct drugs in our 

sample. We omit over-the-counter medications, decongestants, and vitamins since these 

drugs contain numerous chemicals that are difficult to track and change periodically.19 In 

addition, many of these drugs are frequently sold as over-the-counter medications 

through channels not covered well in our dataset. We combine IMS sales data with 

Hatch-Waxman information obtained from the FDA to identify Paragraph Four 

certifications, drugs with exclusivity periods, and dates associated with exclusivity. 

We construct monthly generic drug prices, g

dt p , by aggregating total dollar sales across all 

generic manufacturers for a specific drug and then dividing by the total quantity of pills 

involved in the sale. Following standard practice in the literature (Caves et al. 1991), we 

scale each generic price by the corresponding branded drug’s pre-entry price, b

d p , to 

 

17 IMS Health, IMS National Sales PerspectivesTM, January 2003 to December 2010, Retail and Non-Retail 

Channels. 

18 An oral solid medication is defined as a drug packaged as a capsule or a tablet dosage form. 

19 We also omit any drug with a generic-to-pre-entry revenue ratio greater than 5. 
- 14 - 

arrive at a price measure that is comparable across drugs, / g b

dt dt d p pp  .

20 The pre-entry 

brand price, b

d p , is constructed to be the sales-weighted price per pill during the threemonths preceding generic entry.21 Similarly, we construct market size to be the 

annualized aggregate pre-entry branded dollar sales for each drug during the three months 

prior to generic entry. 

IMS sales information is also used to identify both the number of manufacturers 

marketing the drug and the date of generic entry. We define generic entry as the first 

month in which any generic firm has positive dollar sales in our sample. We limit the 

sample to consider only oral solid prescription medications that begin to face generic 

entry during our sample. We define the number of competitors during each period as the 

number of generic firms with positive dollar sales during the month (after accounting for 

infrequent sales of small firms).22

In order to identify drug sales during exclusivity periods, we combine the IMS sales data 

with information from the FDA’s Approved Drug Products with Therapeutic Equivalence 

Evaluations (a.k.a., the “Orange Book”). The Orange book provides information about 

whether the brand drug faced a Paragraph Four challenge and the period of exclusivity, if 

there is one. 

 

20 Since b

d p corresponds to the brand drug’s price averaged over the three months prior to generic entry, 

the term does not require a time subscript. 

21 Pre-entry brand price is constructed as the total dollar sales of the drug during the 3-months prior to 

generic entry divided by the total unit (i.e., pill) sales during the same period. 

22Our definition of a competitor omits re-packagers and treats several firms with similar names as the same 

firm (e.g., Watson and Watson Labs). In addition, we treat firms as market participants unless observed 

without sales for four consecutive months. The date of exit for these firms is backdated to the last month 

observed without sales. 
- 15 - 

Table 1 presents summary statistics for the drugs used in our analysis. The first two data 

columns provide information for the entire sample of drugs used in the analysis. Data 

columns three and four provide the same information for the sample of drugs observed 

during the 180-day exclusivity period. Data columns five and six provide the same 

information for drugs during the period following an exclusivity period among drugs with 

successful Paragraph Four patent challenges. 

The full sample contains 403 drugs (i.e., molecule-strength-dosage form-therapeutic class 

combinations) representing 146 distinct molecules. Brand sales of drugs with exclusivity 

are, on average, larger than drugs without an exclusivity period. Drugs with exclusivity 

have slightly more than $285 million in annualized pre-entry brand sales, whereas the 

overall sample has $241 million. In addition, not all Paragraph Four challenges 

successfully result in an exclusivity period. Approximately 75% of drugs in our sample 

face a Paragraph Four challenge, but only 42% of drugs have an exclusivity period. 

Notably, drugs with exclusivity periods tend to face less generic competition during 

exclusivity than the full sample of drugs, but face more competition outside of exclusivity 

than the full sample of drugs. For example, Table 1 shows that none of the drugs in our 

sample faces more than three competitors during an exclusivity period (data column 3), 

but drugs that had an exclusivity (i.e., successful Paragraph Four challengers) are more 

likely to face greater than three competitors outside of exclusivity than are drugs in the 

overall sample (comparing data columns 1 and 5). This pattern is consistent with the 
- 16 - 

exclusivity period restricting the number of competitors for precisely those drugs that 

would otherwise attract entry. 

We expand on this point by considering the relationship between the number of 

competitors and market size. Figure 2 compares the relationship between market size and 

the number of competitors for two samples: (1) drugs with exclusivity periods observed 

during the 180-day exclusivity period; and, (2) drugs from the overall sample (including 

drugs with exclusivity periods) observed during the first six months of generic 

competition. Figure 2 plots, separately for the two samples, the average number of 

generic entrants during the first six months after initial generic entry against the sales 

revenue deciles as defined by the entire sample. In the overall sample, higher sales decile 

drugs clearly face more competitors than lower sales decile drugs. Indeed, the two 

highest sales deciles face, on average, at least seven generic competitors, whereas drugs 

in the lowest two sales deciles face, on average, approximately two competitors. The 

strong relationship between market-size and the number of competitors suggests that, in 

the overall sample, market size is an important determinant of entry by generic firms.23

However, during exclusivity, drugs never average more than two competitors, regardless 

of the sales decile. Moreover, the average number of observed competitors is nearly 

constant for the entire sample, regardless of market size. 

 

23 Much of the prior literature (e.g., Caves et al. 1991, Frank and Salkever 1997, and Regan 2008) has 

acknowledged this relationship by using market-size as an instrumental variable in similar specifications. 

Panattoni (2011) and Scott Morton (1999) find direct evidence that market-size is an important determinant 

of generic firm entry. 
- 17 - 

Figure 2 demonstrates that the exclusivity period appears effective at muting the 

explanatory power of an important observed determinant of the number of competitors, 

market size. If the exclusivity period mutes the effects of observed determinants of 

competition, such as market size, it also likely mutes the importance of unobserved 

determinants of competition, as well. This property suggests that the variation in 

competition during exclusivity is likely sufficiently random such that the number of firms 

is exogenous for purposes of our estimation. 

V. Estimation and Results 

In order to determine the effects of competition on generic drug prices, we estimate a 

regression of generic drug prices against a set of drug characteristic controls and the 

number of competitors using equation 1: 

(1)

11 3

1 2

ln ( (1 )) ( )

K K k k

dt k dt dt k dt dt dt x d z i t dt

k k

p man ex man ex X Z     

 

 

          

The dependent variable, ln dt p , is the natural logarithm of the sales-weighted price per 

pill for drug d during month t, normalized by the pre-entry brand price.24 The variables of 

interest are the parameters associated with the number of generic competitors, k

man , 

such that 1 k

man  if the number of competitors is equal to k and zero otherwise.25 The 

number of generic competitors is interacted with whether the observation occurs during a 

 

24 This transformation of the pricing variable allows for an intuitive interpretation of the coefficients of 

interest. For example,  2 = -0.1 implies that a second generic competitor is expected to lower the average 

generic price by 10% relative to a market with a single manufacturer. In addition, this transformation 

provides an infinite support. 

25 Competitive effects from >10 firms are captured using a single indicator. 
- 18 - 

180-day exclusivity period, ex . The coefficients on the exclusivity interaction terms, k  , 

represent the effects of competition during exclusivity, and the coefficients on the nonexclusivity interaction terms, k  , represent the effects of competition outside of 

exclusivity. 

The model includes fixed effects for the active ingredient (molecule) of the drug, i , and 

the calendar month, t .

26 The regression also includes controls for drug characteristics 

that vary over time within a drug, represented by X, such as the age of the drug since 

generic entry.27 We also control for drug characteristics that vary across drugs within a 

molecule but are fixed over time, such as dosage form, which is represented as Z. In 

addition, we account for correlation in prices over time and within molecules using 

standard errors that cluster at the molecule level. 

We limit the sample to the first 18 months of generic drug marketing. The 18-month 

period is long enough to provide significant pricing variation following the exclusivity 

period, but is also comparable to the timing and duration of the exclusivity period, which 

is only six-months and always occurs immediately following the initiation of generic 

competition.28 We exclude the first and seventh month of generic competition since the 

 

26 The number of competitors does not vary over time within a drug during the exclusivity period. 

Consequently, our model cannot include drug fixed effects and separately identify the effects of 

competition. 

27 We define age as the number of months since the drug began facing generic entry. The variable, X, also 

includes a constant. 

28 The results are not sensitive to this time period restriction. Estimates from models with as many as 24 

months and as few as 12 months of generic competition have quantitatively similar results. 
- 19 - 

first month often represents a month of partial sales and month seven sometimes 

represents a mix of exclusive and non-exclusive days. 

Table 2 presents the baseline results of the effects of competition on generic drug prices. 

The marginal effects of an additional competitor are reported separately for the periods 

inside and outside of the 180-day exclusivity period (i.e., the estimates k  and k 1  

from equation 1, respectively). All effect estimates (i.e., inside and outside of exclusivity) 

are reported separately for three samples that correspond to different control groups. The 

first data column represents the effects measured from the full sample of drugs and 

provides the standard errors beneath the coefficient estimates in parentheses. Data 

column 2 is the analogous result from the sample of drugs facing a Paragraph Four 

challenge. Limiting the sample to drugs facing Paragraph Four challenges reduces the 

selection bias associated with having an exclusivity period, since all drugs in the 

Paragraph Four sample are selected in the same way (i.e., they all face a Paragraph Four 

challenge). The last data column represents the sample of drugs that face successful 

Paragraph Four challenges (i.e., all drugs in the sample have an exclusivity period). This 

sample is unlikely to have any selection issues since nearly every drug in the sample has 

both a period during exclusivity and a period outside exclusivity.29 However, this 

limitation also reduces the size of the sample and results in dramatically larger standard 

errors outside of the exclusivity period. 

 

29 Drugs introduced after April 2010 are only observed during the exclusivity period. 
- 20 - 

The coefficient estimates suggest that the effect of two competitors lowers prices from 

between 13.7% and 22.6%, relative to a single competitor. Although these effects are 

large in magnitude, they are imprecisely estimated and statistically insignificant (at the 

5% confidence level) in all models, both inside and outside of exclusivity. In contrast, the 

effect of three competitors is statistically significant in all specifications, both inside and 

outside of exclusivity. The effects of three competitors are always large in magnitude, 

ranging from between -31.5% and -54.3%, but are larger during the exclusivity period 

than they are outside of exclusivity. Indeed, in the full sample of drugs, the effect of three 

competitors is 17 percentage points larger during exclusivity than outside of it. Although 

we find economically large differences between the exclusivity and non-exclusivity 

periods, the difference estimates are imprecisely estimated and are rarely statistically 

significant. 

Next, we consider the effects of competition separately for large and small drugs. 

Allowing the effects to vary by the size of the drug accounts for demand side conditions 

that may affect the nature of competition. We define a large drug to be any drug with a 

market size greater than the mean of our sample, where market size is measured as the 

annualized pre-entry branded sales in the three months prior to generic entry. The 

analysis is performed by interacting whether a drug is large with the competitive 

variables of interest. Specifically, we estimate equation 2: 

(2) 

11 3

1 2

11 3

1 1

ln [ ( (1 )) ( )]

(1 ) [ ( (1 )) ( )]

K K lk lk

dt dt k dt dt k dt dt

k k

K K sk sk

dt k dt dt k dt dt dt x d z i t dt

k k

large

larg

p man ex man ex

e man ex man ex X Z u

 

    

 

 

 

 

     

         

 

 
- 21 - 

Equation 2 is analogous to equation 1, except that we include interaction terms of the 

competitor variables with an indicator for whether the drug is large, large. The omitted 

category is the effect of one competitor during exclusivity for large drugs. Table 3 reports 

the effects of two and three competitors on prices during and outside of the exclusivity 

period, separately for large and small drugs. During the exclusivity period, the marginal 

effects of two and three competitors are always larger for large drugs than they are for 

small drugs. However, outside of exclusivity, a comparison of the marginal effects 

between large and small drugs reveals no pattern. 

The effect of two competitors is never statistically significant in any of the samples, large 

or small. However, the point estimates suggest that two competitors can lower prices 

anywhere between 4.1% and 37.7%, depending on the sample and whether the drug is 

inside or outside of exclusivity. The effects from two competitors are typically larger 

during exclusivity than outside of it, except in the case of the sample of large drugs 

limited to drugs with exclusivity periods. 

The magnitude of the effects of three competitors, relative to a single competitor, are 

always very large, both inside and outside of exclusivity. Estimates of the effects range 

between 18.0% and 73.3%, depending on the sample and whether the drug is observed 

during or outside of exclusivity. During exclusivity, the p-values for the effect estimates 

never exceed 5.2%, and thus the results are statistically significant, or almost statistically 
- 22 - 

significant, in every sample.30 However, the effects of three competitors are always larger 

during exclusivity than outside of exclusivity. These differences can be large in 

magnitude. Among large drugs, the differences exceed 35 points in two of the three 

samples.31 The effect of three competitors during exclusivity is also larger among large 

drugs than among small drugs in all three samples. Moreover, the differences between the 

effects inside and outside exclusivity are more pronounced among larger drugs than 

among smaller drugs. Among large drugs, the differential is statistically significant in two 

of the three samples, but the differential is never statistically significant among small 

drugs. 

These results suggest that failure to control for endogenous entry could potentially 

underestimate the effects of three competitors on prices, especially among large drugs. In 

addition to affecting the direct inference for these competitors, the underestimated effects 

could potentially lead to overestimates of the marginal effects attributed to later 

competitors.32 Further exacerbating this issue is the observation that the marginal effects 

of two and three competitors are typically larger (during exclusivity) among large drugs 

then they are among small drugs. If large drugs eventually attract more competitors, then 

failing to account for these demand-side factors could also lead to biases that overstate 

the effects of later competitors, as illustrated in our discussion of Figure 1. Numerous 

 

30 The p-value is 5.2% during exclusivity for the small drug full sample and the small drug sample of 

successful paragraph IV drugs. The effects of three competitors during exclusivity is always statistically 

significant among large drugs. 

31 The difference is not statistically significant among large drugs with successful paragraph IV challenges. 

32 To see this, suppose that the cumulative effect of four competitors is estimated accurately (e.g., 75%), but 

the cumulative effect of three competitors is underestimated (e.g., estimate = 50% and true value = 70%). 

The estimated marginal effect of the fourth competitor would be 25%, but the true marginal effect would be 

5%. 
- 23 - 

papers have found that larger drugs attract more competitors outside of exclusivity (see 

e.g., Scott Morton 1999 and Panattoni 2011). Consequently, our results suggest that both 

the differences in the effects between large and small drugs, and the differences in effects 

during and outside of exclusivity among both large and small drugs, could lead to 

overestimates of the effects of later competitors. 

Instrumental Variables and the Exclusivity Period 

The exclusivity period restricts entry such that only a few competitors compete for drugs 

that would normally attract many entrants. However, the number of competitors observed 

during exclusivity may still be endogenously determined if, for example, low-cost drugs 

systematically attract an authorized generic competitor, or manufacturers with wider 

portfolios of drugs command a pricing premium over manufacturers with limited 

portfolios.33 We address these potential sources of endogeneity during exclusivity by 

modeling the number of competitors during the period using instrumental variables (IV). 

Table 4 provides motivation for using an NCE indicator and the natural log of market 

size as instruments. Market size and NCE have are positively correlated with the number 

of generic competitors during the exclusivity period, with correlations of .20 and .23, 

respectively. The first data column of Table 4 provides the mean of market size in 

millions of dollars, conditional on the number of generic competitors. Average market 

 

33 As an example of portfolio effects, one might believe that OLS could incorrectly estimate the effects of 

competition within a molecule if one of the competing manufacturers offers the full suite of dosage forms 

during exclusivity, whereas the other competing firm offers only a subset of them. 
- 24 - 

size nearly doubles between one and two competitors, although two and three 

competitors have similar market sizes. The third and fifth columns present the 

distribution of the number of manufacturers among drugs without and with an NCE, 

respectively. Half of drugs without an NCE designation are marketed by a single 

manufacturer during the exclusivity period. In contrast, only 29% of NCE drugs are 

marketed by a single manufacturer. 

Our IV specification considers the effect of generic competition on generic drug prices 

during the exclusivity period by estimating a two-stage model represented by equations 3 

and 4: 

(3) (stage 1) mX Z V dt dt x d z d i t dt         

(4) (stage 2) ln ˆ dt dt dt x d z i t dt p mX Z          

The first stage dependent variable, mdt , is the number of generic manufacturers 

competing for sales in drug d during month t .

34 The second stage dependent variable is 

the natural logarithm of the relative generic drug price for drug d during month t , the 

same dependent variable as in equation 1. 

The first stage models the number of competitors as a function of fixed and time-varying 

drug characteristics, represented by X and Z, respectively. We also include molecule and 

calendar-month fixed effects, which are represented as i  and t  , respectively. The drug 

 

34 One drawback of a linear specification in the first stage is censoring. Replacing mdt with ln mdt , a loglin specification, provides an infinite support. A log-lin specification in the first stage and log-log in the 

second stage produces the same conclusions as the specification described above. 
- 25 - 

characteristics and fixed effects for molecule and time are also included in the second 

stage pricing equation, and are analogous to the variables included in equation 1. 

However, time and age effects are limited to reflect that the sample includes only those 

periods that occur during 180-day exclusivity. Stage 1, equation 3, also includes a set of 

excluded instruments. The set of instruments excluded from the price regressions are 

fixed drug characteristics, represented by Vd in equation 3. 

The second stage pricing equation models price as a function of the same drug 

characteristics and fixed effects included in equation 1. However, we limit attention to 

the exclusivity period and estimate the effect of generic competition using the coefficient 

on the manufacturer term, ˆmdt , which is predicted from the first stage estimation results. 

Although no drug faces more than three competitors (see Table 1) during the exclusivity 

period, our IV specification imposes a log-lin relationship between competition and 

prices, such that the marginal effect of the third manufacturer is the same as the second 

manufacturer. Since this specification does not enable us to directly compare the IV 

results to those provided in Tables 2 and 3, we also separately estimate an OLS 

regression that also imposes a log-lin specification. We use compare results from this 

estimation to those of the IV estimations to identify any potential biases associated with 

the exogenous treatment of competition, and the direction of that potential bias. 

We estimate three IV specifications such that each specification represents the same 

model (i.e., equations 3 and 4) using a different set of instruments. The first specification 

employs as an instrument a discrete variable that indicates whether the FDA designated 
- 26 - 

the drug a NCE. The second specification considers the natural logarithm of market size 

as an instrument. The third specification includes both the NCE designation and market 

size as instruments. 

Table 5 reports estimates of effects from additional generic competitors on prices from 

these models, alongside estimates from a log-lin specification without instrumentation, 

but during exclusivity. The IV manufacturer coefficient estimates are are quite close 

together and are always larger than the OLS coefficient estimate. These results suggest 

that the exogenous treatment of entry during the exclusivity period understates the 

average effect of an additional generic competitor. Moreover, these differences are large 

in magnitude. Indeed, the IV estimates are three times as large as the OLS estimate. The 

differences between the NCE-only specification and the OLS specification are the most 

modest, but even this comparison suggests that OLS severely underestimates the average 

effect of an additional generic competitor. 

When market size is the only excluded instrument, the specification provides the largest 

effects. However, the estimate is imprecise and is statistically significant at the 10% 

level. In contrast, all the effect estimates from specifications that include the NCE 

designation are statistically significant. Table 5 also reports some diagnostic statistics that 

enable us to evaluate the suitability of our proposed instruments. An instrument that is 

both correlated with the variable of interest (i.e., the number of manufacturers) and 

uncorrelated with the principal error term (here, generic drug prices) after controlling for 

the other observables is relevant and valid. The first stage F statistics provide a measure 
- 27 - 

of the instruments’ relevance, i.e., whether the set of instrumental variables correlates 

with the variable of interest, the number of competing generic drug manufacturers. By 

this measure, the NCE indicator is a relevant instrument. Both specifications that include 

the NCE instrumental variable have first-stage F values that are statistically different 

from zero at traditional levels of significance. In addition, each specification that includes 

the NCE has a first-stage F-statistic that is greater than 10.35 These results suggest that the 

NCE instrument successfully predicts the number of competitors, and thus satisfies the 

relevance criterion for an instrument. 

Although much of the previous literature has used market size to instrument for the 

number of competitors outside of exclusivity, our results during exclusivity suggest that 

market size is a weak instrument. We cannot reject the hypothesis at any conventional 

level of statistical significance that market size alone does not explain the number of 

competitors.36 However, the specification that includes both market size and the NCE 

designation has a first-stage F-statistic that is larger than 10, but is smaller than that of the 

NCE alone. 

In the over-identified model (i.e., the model that includes the NCE indicator and market 

size), we also report the Hansen J-statistic for over-identification. We use this statistic to 

test whether the instruments are appropriately excluded from the second stage regression. 

 

35 Stock et al (2002) suggest that first stage F statistics less than 10 may indicate that the instrument set is 

not relevant or “weak,” even if the first-stage F-statistic is statistically significant at conventional 

significance levels. 

36 As we demonstrate in Table 4, we find an unconditional correlation between market size and the number 

of competitors during exclusivity. However, controlling for drug characteristics, such as molecule fixed 

effects, attenuates this relationship. 
- 28 - 

With a p-value of 0.95, we fail to reject that the J-statistic is different from zero in the 

over-identified specification. Failure to reject provides some statistical support that our 

instruments satisfy the validity criterion, i.e., that the set of instrumental variables is 

uncorrelated with the error term in the price regression. 

To the extent that the diagnostic statistics are informative about the validity and relevance 

of our instruments, there is strong evidence in favor of using the NCE indicator as an 

instrument since it is correlated with the variable of interest and uncorrelated with the 

error-term in the second stage. Although some evidence suggests that market size is a 

weak instrument and should not be used as an instrument during the exclusivity period, 

the similarity of the IV point estimates and the statistics in the over-identified 

specification weakly support its inclusion. 

Regardless of which instrument set we consider, the coefficient estimates from our IV 

regressions predict larger competitive effects than our OLS estimates during exclusivity. 

Since our OLS estimates during exclusivity predict larger competitive effects than our 

OLS estimates outside exclusivity, these results imply that the competitive effects outside 

of exclusivity may understate the effects of price by the earliest entrants in the 

competitive sequence. 
- 29 - 

VI. Conclusion 

The relationship between competition and generic drug prices is a fundamental issue for 

understanding rising drug costs. This relationship has important implications for merger 

enforcement and health care cost containment policy. We demonstrate that endogenous 

entry may introduce important biases in the estimated relationship between price and the 

number of generic competitors. Consequently, careful empirical analysis is necessary to 

identify this relationship. 

We control for potentially endogenous drug entry by exploiting both the 180-day 

exclusivity period awarded to generic drug applicants, and the filing prohibition 

associated with a drug designated as a new chemical entity (NCE). We find that an 

additional competitor lowers generic drug prices by a greater extent during the 180-day 

exclusivity period than outside of it. We interpret this finding as evidence of bias in the 

estimates of generic entry performed outside of the exclusivity period, where endogenous 

entry is uncontrolled. These differences are economically important in all samples, and 

among large drugs, the differences are often statistically significant. 

Using the NCE designation as an instrumental variable during the exclusivity period 

suggests that even the effect estimated during the exclusivity period may understate these 

effect estimates, exacerbating the effects of endogeneity bias. 
- 30 - 

Although a great deal of empirical work has attempted to estimate the relationship 

between competition and prices in the generic drug industry, our results suggest that 

endogenous entry introduces an attenuation bias in the estimates of the effects of the 

second and third competitors on price, which biases marginal effect estimates of later 

entrants. Moreover, our results suggest that the bias is potentially very large, especially 

among high revenue drugs. 
- 31 - 

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2 3 4

Number of Manufacturers

Always less than 5 manufacturers Eventually 5 manufacturers or more

 IMS

Separately by entry profile

gure 1: Relative price by number of manufacturers

in first 24 months of generic competition
0 100 200 300

average market size (millions $)

3 4 5 6 7 8 9 10

market size deciles

average competitors for drugs without exclusivityaverage competitors for drugs with exclusivityaverage pre−generic entry market size

 IMS during the first six months of generic competition.

by exclusive and non−exclusive drugs

onship between market size and number of generic competitors
Table 1: Summary statistics 

Variable Mean SD Mean SD Mean SD

Time-Varying Characteristics

Generic Price Relative to Pre-Entry Brand Price 0.478 0.299 0.735 0.135 0.433 0.2

Average Number of Manufacturers 4.33 3.62 1.72 0.72 4.77 3.4

Generic Competitors = 1 0.227 0.419 0.440 0.497 0.174 0.3Generic Competitors = 2 0.211 0.408 0.404 0.491 0.154 0.3Generic Competitors = 3 0.130 0.336 0.156 0.363 0.154 0.3Generic Competitors > 3 0.432 0.495 0.0 0.0 0.518 0.50

Fixed Characteristics

Faced a Paragraph IV Challenge 0.750 0.434 1.000 0.000 1.000 0.00

Market Size (Millions) 241 406 285 415 292 41

Tablet 0.610 0.488 0.532 0.500 0.571 0.49

Capsule 0.116 0.321 0.129 0.336 0.103 0.30

Chewable Tablet 0.009 0.094 0.012 0.108 0.013 0.1

Orally Disintegrating Tablet 0.050 0.218 0.064 0.246 0.058 0.2

Extended Release Capsule 0.047 0.211 0.064 0.246 0.051 0.22

Extended Release Tablet 0.168 0.374 0.199 0.400 0.205 0.40

Unique Molecules 146 58 53

Unique Drugs 403 171 156

Drug-Months < 25 Months Since Entry 8413 788 2533

Notes: Data source IMS Health National Sales Perspective April 2003 - December 2010 (IMS). Pre-entry brand prices calculated using 3 

months prior to generic entry. Market size is the annualized sales of brand drugs for the 3 months prior to generic entry and is measured in millions of December 2008 dollars. Competitor count is the contemporaneous number of competitors during the period.

Drugs with Exclusivity Periods

All Drugs During Exclusivity Outside Exclusi
Table 2: Estimates of the effects of a second and third generic competitor on generic drug prices

Variables Overall Paragraph IV Drugs with Exclusivity

Price Effects Outside Exclusivity

Two Competitors -0.137* -0.226* -0.195

(0.077) (0.134) (0.183)

Three Competitors -0.315** -0.429** -0.509**

(0.109) (0.172) (0.227)

Price Effects During Exclusivity

Two Competitors -0.134 -0.160 -0.158

(0.159) (0.170) (0.171)

Three Competitors -0.483** -0.518** -0.543**

(0.189) (0.196) (0.179)

Adjusted R-Squared 0.928 0.923 0.921RMSE 0.262 0.274 0.242Clusters 146 80 58N 5993 3766 2301

Notes: *Statistically significant at the 10% level. **Statistically significant at the 5% level. Data source IMS. The sample is limited to the first 18 months of generic competition. All models include molecule and calendar-month fixed effects. Overall and Paragraph IV drug samples also include age fixed effects. Robust standard errors that are clustered by molecule are reported. The effect of one competitor outside of exclusivity, 1, is -0.07, 0.04, and -0.02 for the samples, respectively.
Variables Small Large Small Large Small Large

Price Effects Outside Exclusivity

Two Competitors -0.127 -0.041 -0.218 -0.171 -0.155 -0.377

(0.083) (0.106) (0.140) (0.115) (0.183) (0.299)

Three Competitors -0.306** -0.180 -0.421** -0.347** -0.538** -0.490

(0.119) (0.146) (0.195) (0.147) (0.268) (0.294)

Price Effects During Exclusivity

Two Competitors -0.239 -0.317 -0.291 -0.347 -0.201 -0.269

(0.256) (0.277) (0.274) (0.291) (0.267) (0.272)

Three Competitors -0.556* -0.664** -0.610** -0.733** -0.578* -0.626**

(0.284) (0.281) (0.303) (0.290) (0.292) (0.266)

Adjusted R-Squared 0.930 0.925 0.922RMSE 0.260 0.270 0.240Clusters 146 80 58N 5993 3766 2301

Notes: *Statistically significant at the 10% level. **Statistically significant at the 5% level. Data source IMS. The sample is limited to the first 18 months of generic competition. All models include molecule and calendar-month fixed effects. Overall and Paragraph IV drug samples also include age fixed effects. Robust standard errors that are clustered by molecule are reported.

Table 3: Estimates of the effects of a second and third generic competitor on generic drug prices, separately for "small" and "large" drugs

Overall Paragraph IV Drugs with Exclusivity
Table 4: Correlation of excluded instruments and competitor count during the exclusivity per

Market Size SE Non-NCE SE NCE 

1 Competitor 185.6 324.0 0.506 0.503 0.291 02 Competitors 365.9 485.1 0.341 0.477 0.419 03 Competitors 326.1 405.0 0.153 0.362 0.291 0CorrelationDrug Count 85 86

0.230

Notes: Data source IMS. Market size is the annualized sales of brand drugs for the quarter prior to generic entry measumillions of dollars. Competitor count is the maximum number of competitors marketing the drug during the exclusivity 

0.153
Table 5: OLS and IV estimates of the effects of an additional competitor on generic drug prices

Estimator OLSInstruments NCE ln(MS) NCE & ln(MS)

Coefficient Estimate Estimate Estimate Estimate

Second Stage Estimates (Dependent Variable = ln (Price))No. of Manufacturers -0.102** -0.296** -0.308* -0.298**

(0.014) (0.046) (0.178) (0.049)

First Stage Estimates (Dependent Variable = Manufacturer)NCE n/a 1.873** n/a 1.835**

(0.360) (0.355)

ln(MS) n/a n/a 0.034** 0.030*

(0.016) (0.015)

Estimation DiagnosticsFirst Stage F n/a 27.1** 4.7** 15.4**J Statistic n/a n/a n/a 0.005P-value of J Statistic n/a n/a n/a 0.946First-stage Partial R2 n/a 0.098 0.019 0.113

Clusters 58 58 58 58N 788 788 788 788

Notes: *Statistically significant at the 10% level. **Statistically significant at the 5% level. Data source IMS. All modelsinclude molecule, month and age fixed effects. Robust standard errors that are clustered by molecule are reported.

Instrumental Variables