Competition and Innovation Revisited: A Product-Level View

Jon A. Garfinkela

Mosab Hammoudehb,*

First Draft: August, 2020

Current Draft: June, 2021

Abstract

We study the effect of competition on firm innovation at the product level. We instrument shocks to competition in therapeutic areas with the FDA’s breakthrough therapy designation (BTD) event on a therapy. BTD events strongly associate with several indicators of future success, including announcement returns and eventual FDA approval to market the drug. BTD shocks discourage rivals’ innovation in that therapeutic area on average. However, the effect varies with ex-ante competitiveness of the therapeutic area, as well as with the rival’s position (leader vs. follower) in that space. Our evidence is consistent with the theory in Aghion et al. (2005).

* Corresponding author

aProfessor of Finance, Department of Finance, University of Iowa, jon-garfinkel@uiowa.edu

bPhD Candidate, Department of Finance, University of Iowa, Mosab-hammoudeh@uiowa.edu

We thank our discussant at the 2021 MFA conference (Giorgo Sertsios) for thoughtful suggestions. We are especially grateful to both Dennis Erb and Richard Peter for their consistent feedback as well as (numerous) patient explanations. We thank Brandon Boyd, Ketan Patel and Max Penverne at Cortellis (Clarivate) for data provision and guidance, and Zaid Assaf for research assistance in assigning drugs to ICD-10 codes. All errors are the responsibility of the authors. This paper previously circulated under the title “Competition Threats and Rival Innovation Responses: Evidence from Breakthrough Therapies”.

mailto:jon-garfinkel@uiowa.edumailto:Mosab-hammoudeh@uiowa.edu

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I. Introduction

The relationship between competition and innovation is of crucial interest to academics,

regulators, firms and consumers. It carries the potential for both company and individual windfalls, it can

guide policy, and has broad economic and societal influence. Yet the shape of that relationship remains

inconclusive with oft-conflicting empirical results,1 as well as varying theoretical perspectives.2 Perhaps

this is unsurprising. Empirically, the competitive landscape continuously shifts with globalization, while

innovation alters firm - even industry - boundaries, and these combine to complicate measurement of

both. On the theory side, the most prominent model that accommodates both positive and negative

correlations is Aghion et al. (2005). But it does so with clear recognition of the endogenous relationship

between the two, implying the importance of appropriate shocks for testing. Finally, there is an underlying

complication that influences both measurement and conceptual understanding of the relationship; firms

typically operate in more than one product market, with each facing varying competition levels and

industry-positions, all affecting optimal firm responses.

This paper revisits the empirical relationship between competition and innovation. Specifically,

we study positive competitive shocks within the pharmaceutical industry and their influence on innovative

activity in that industry. The benefits of our focus on a single industry are several. Pharma offers a window

into individual health and company economic windfalls. Corporate investment activity in the industry is

highly innovation oriented. The shock we study is enabled by government policy. Perhaps most

importantly, pharmaceutical data are available at the product/project level. This allows measurement of

competitive environment, firm position, and innovative activity, all with the granular detail necessary to

avoid aggregation challenges.

1 Hombert and Matray (2018), Hoberg et al. (2019) and Autor et al. (2020) document negative relationships between competition and innovation. Phillips and Zhdanov (2012), and Bloom et al. (2016) document positive relationships. 2 For the negative relationship, see Schumpeter (1943), Salop (1977) Dixit and Stiglitz (1977), Romer (1990), Aghion and Howitt (1992), and Grossman and Helpman (1991). The contrasting view of a positive relationship is presented in Hart (1983) via agency considerations, and by Aghion et al. (2001) with step-by-step innovations.

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On July 9, 2012, the Food and Drug Administration (FDA) introduced a new expedited pathway

program named the breakthrough therapy designation (BTD) program. It is designed to facilitate and

expedite the approval of therapies that have demonstrated substantial improvements over available

treatments for a given therapeutic market (i.e., medical condition) (Sherman 2013). Empirically, we use

BTD designations as shocks to competition within a therapeutic market, and then examine the product

level innovation responses of rivals. Further specifically, we examine rivals’ drug project continuation

decisions, as well as new drug project initiation decisions, after BTD shock(s).

I.A. Summary of Plan and Results

We begin with analysis and discussion on the validity of BTDs as a shock to competition. We argue

in favor because (1) their typical event timing is early enough in the drug-development process that the

news of the drug’s efficacy is a surprise; (2) BTD drugs are more likely to be approved for sale and

subsequently dominate the therapeutic markets they compete in; and (3) because they bring significant

financial success to their sponsors, rendering them more capable competitors.3

BTDs are most commonly awarded during phase-II clinical trials, before large-scale production

and trial enrollments. This limits awareness of the therapy by competitors since phase-II trials usually

involve less than 100 participants. Furthermore, the designation is only awarded to those who meet its

stringent requirement. This means that even when competitors are aware of the development of a

competing drug, they cannot accurately predict whether the drug will receive the designation before that

news is made public.

The medical literature finds that physicians are more likely to prescribe BTDs relative to non-BTDs

for a given medical condition, and that patients are more likely to request of physicians a BTD relative to

a non-BTD drug. BTD firms experience significantly positive abnormal stock returns, and rival firms

3 See section II.B below for full details, but we briefly summarize next.

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experience significantly negative ones, around BTD announcement dates.4 Finally, since BTDs are granted

early in the development process with potentially several years left before FDA approval to market the

drug, we can explore approval hazards. BTDs are more likely to be approved (and faster) relative to non-

BTDs. Taken together, the evidence supports the claim that BTDs are shocks to competition.

We then examine rival firm innovation responses to BTD shocks.5 Our context is the theoretical

model in Aghion et al (2005). It predicts that the relationship between competition and innovation

depends on both the general competitiveness of a product-market (industry), and the firm’s competitive

position within it – i.e., whether the firm is a leader or a laggard in that market. To characterize the

tensions in their model, consider a single-market example.6 If competition in that product market is ex-

ante high, then a laggard (i.e. follower) firm has difficulty realizing economic profits. A shock increase in

competition exacerbates this, discouraging market followers from innovating (while leaders are less

affected).7 On the other hand, when competition in the shocked market is ex-ante low, an increase in

competition may encourage market followers to innovate in order to catch up with market leaders and

reap rents. In aggregate, a shock increase in competition will discourage innovation in ex-ante more

competitive markets, and vice-versa, with both effects driven by laggard responses.

To test these predictions, we require measures of competition, innovation, and a firm’s position

(leader or laggard), for each therapeutic market. Our primary measure of competition is the number of

drug projects in a therapeutic area. We measure innovation in two ways. First, we focus on the within-

industry view of the Aghion et al. model, and study development continuation of drug projects from

phase-II to phase-III of clinical trials. We then extend our analysis to include drug project initiations. These

can be either in the shocked market or an alternative market, which we recognize below. We define

4 This subset of results only applies among publicly traded drug companies. 5 We define rivals as firms who own products at any stage of development in the BTD-shocked therapeutic area. 6 This aligns with their framework’s exploration of innovative responses to competitive shock within an industry. Their within-sector analysis underpins their overall conclusions regarding the competition-innovation relationship. 7 We discuss our hypotheses and how they stem from Aghion et al. (2005) more fully in Section III.

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market leaders as firms with an approved-for-sale product in the shocked therapeutic market. Followers

are firms with no approved-for-sale products in the shocked therapeutic market, but with projects under

development in it. Note that this definition allows the same firm to be a leader in some markets and a

follower in others, providing important within-firm variation.

Our continuation results are consistent with the model by Aghion et al (2005). Broadly, innovation

in response to the shock varies with market competitiveness and rival position in that market. Hazards

explaining migration from phase-II to phase-III are decreasing in the shock. BTD events discourage rivals’

innovative activity in that product (therapeutic) market. However, the discouragement is isolated within

ex-ante more competitive therapeutic markets. When a market is ex-ante less competitive, the shock

actually encourages innovative activity, speeding up hazards from phase-II to phase-III. Finally, we

document the model’s expected variation in follower (laggard) innovative behavior in response to shocks,

across the more versus less competitive product market environments. When the market is ex-ante less

competitive, a BTD shock tends to unlevel it but it is then expected to quickly re-level as the laggard

innovates, which we document in our data. On the other hand, when a therapeutic market is ex-ante

more competitive, there is little incentive for a laggard to innovate because of limited rents to catching

up. A BTD shock does not encourage rival laggards to innovate to catch up given lower rents available in

such markets and may even discourage them. We find this in our data as well. Overall, BTD shocks

encourage rival laggards to accelerate development of phase-II projects to phase-III in less competitive

markets, while discouraging them in more competitive markets. Taken together, these results align with

the model in Aghion et al. (2005).

The advantage of focusing our continuation tests on phase-II to phase-III development is the

greater homogeneity of the test sample and therefore the firm’s decision criteria. It aligns with the

characterization of within-industry innovative responses to competitive shocks in Aghion et al. (2005). Yet

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the cost is that this sample has drug projects that evince non-trivial progress, so one might reasonably

question the label of innovation.

To address this concern, we also examine new drug project initiations. We find that BTD

(competitive) shocks encourage rivals in the shocked therapeutic area to launch a new drug discovery

project. In particular, the more shocks that a rival experiences in markets where they had a project [during

the last one (or two) years], the higher the likelihood it begins a new drug discovery project. In other

words, rivals innovate with brand new projects in response to BTD shocks.

However, the new project could be within the shocked market or it could be in a different

therapeutic area. We explore this second level of choice – where to locate the new drug project – by

conditioning on all new drug discovery project events.8 We then explore the determinants of whether

that new drug discovery project is in the shocked area or a different area. Laggards – in particular – place

such new drug discovery projects on average in a less competitive market than the shocked one. This is

consistent with the intuition of Aghion et al. (2005).

Overall, the continuation results and the new drug discovery project results provide robust

evidence in support of Aghion et al. (2005). The relationship between competition and innovation

depends on both the ex-ante competitiveness of the market as well as the position of the firm. Our

product-level data provides the first granular product-level evidence on this relationship, uncontaminated

by firm-level or industry-level aggregation of innovative activity measures.

I.B. Contributions to Extant Literatures

We begin with the broadest view – the relationship between competition and innovation. Many

papers have explored this.9 Our paper’s empirical design offers two incremental advantages. First, most

8 Within (either) one (or two) year(s) of a BTD shock by a rival. 9 Again, see Aghion et al. (2005), Autor et al. (2020), Bloom et al. (2016), Blundell et al. (1999), Hoberg et al. (2019), Hombert and Matray (2018), Nickell (1996), Scherer (1967), to name a few.

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papers use patenting activity or R&D spending as a proxy for innovation. The former may result in

attenuation measurement error since patents are only one potential outcome of the R&D process.10

Furthermore, R&D spending (as a proxy for innovation) does not capture the type of R&D conducted nor

the distribution of spending across the firm’s product markets, because R&D spending is usually

aggregated into a single line item on a firm’s financial statements. We refer to this as the aggregation

problem. Second, most prior papers measure competition shocks at the industry, or firm, level. This may

also potentially amplify the aggregation problem since most firms compete in several product markets

with each carrying a different level of importance to the firm.

We overcome the aggregation challenge by focusing on project level data that is allowed to vary

within firm. We mitigate the attenuation problem by relying on pharma-industry practices of reporting

key project milestones before product approval and sale. Nevertheless, a potentially valid concern is

generalizability of our results. While we cannot observe project-level investment outcomes (milestones)

in other industries, we can report that our project level results aggregate up in a therapy market (i.e.,

industry) to mirror the broad patterns observed in Aghion et al. (2005).11 To the best of our knowledge,

we are the first to examine innovation as a response to competition shocks at the product level.12

A closely related literature explores the effect of regulation on innovation, through competition.

Recent work in this area includes Aghion et al. (2021), Hermosilla (2020)13, Li, Lo and Thakor (2020), and

Lo and Thakor (2020). However, these papers largely view the shock as regulatory and the mechanism is

10 Furthermore, while patents are essential in some industries, they are not very common in others like textiles and amusement devices (Moser 2012, Sukhatme and Cramer 2019). 11 We show in section V.A. that the relationship between therapeutic market competition and the percentage of phase-II projects that reach phase-III (averaged across the full sample period, in a purely cross-sectional framework) presents as an inverted-U. This aligns with their model’s inverted-U relationship between competition and innovation, but in the context of the pharma industry. 12 While Cunningham et al (2021) use product-level data to examine firm response to nascent competition, they focus on the role of acquisitions in thwarting competition. 13 Who also studies the BTD regulation, but as a single-event shock.

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through variation in firm sensitivity to the shock14. By contrast, our approach permits a direct view of the

competition-innovation relationship. The regulation simply enables the shock, but we are able to exploit

both time-series and cross-sectional heterogeneity in treatment.

Our paper naturally fits in the literature on rival responses to entry threats.15 The extant work

finds mixed evidence on whether incumbents are more likely to deter or accommodate entry. This may

be due to either homogeneity of event-shock, or relative silence on one of the two elements from Aghion

et al. (2005), which we show both matter. Perhaps closest in spirit from this literature is Aboulnasr et al.

(2008). They study rival responses to ‘radical’ product innovations in the pharmaceutical industry (using

the FDA’s priority review expedited approval program). Rival responses depend on introducer-firm size

and market-dependency. Our paper offers several advantages. First, our shock occurs earlier in a product

development life-cycle, supporting the presumption of the surprise nature of the shock. Second, we use

a more dynamic measure for a rival’s innovative response.16 Third, we have a larger sample with greater

cross-sectional and time-series heterogeneity. Most importantly, our analysis is grounded in Aghion et

al.’s theory and tests the product-market-level drivers of it.

Finally, we contribute to the literature on drug project development and discontinuation

decisions. Krieger (2021) examines the impact of competitors’ project development failures on a firm’s

project continuation decisions. Krieger et al. (2018) examine the impact of public health advisory (PHA)

disclosures on competitor R&D activity. These are responses to negative product outcomes. Meanwhile,

14 A notable exception is Aghion et al. (2018), who provide experimental evidence on the relationship. We view our work as complementary to theirs. While they are able to identify more precisely through experimental controls, we provide evidence from firm operational decisions. 15 For example, Walmart entry (Khanna and Tice 2000, 2001), airline firm entry (Goolsbee and Syverson 2008, Parise 2018, Kwoka and Batkeyev 2019, Ethiraj and Zhou 2019), foreign products entry (Frésard and Valta 2015) generic drug entry (Tenn and Wendling 2014), bank entry (Tomy 2019), and to the threat of Google’s entry into the app market (Wen and Zhu 2019). 16 Aboulnaser et al (2008) measure rival response as rival product launch, which take an average of 5 years from chemical compound discovery to FDA approval. We however measure rival response as development continuation from phase-II to phase-III which takes about 2 years. Furthermore, we measure the innovation response at the product market (i.e. therapeutic) level instead of aggregating the response at the firm level as they do.

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Cunningham et al. (2021) find that firms thwart future competition by acquiring competitors and

discontinuing their similar drug projects that were under development17. Finally, Guedj and Scharfstein

(2004) find that smaller biotech firms are more likely to advance lower quality phase-II clinical trials,

relative to large pharmaceutical companies. None of the above papers contemplate our setting with

positive exogenous competitive shocks, and responses that are influenced by ex-ante industry

competitiveness and responder position within it.

II. Breakthrough Therapy Designation and Competition in the Biopharmaceutical Industry

This section offers several perspectives on the exogeneity of our competition shock proxy – drug BTDs.

We begin with a brief description of the program’s institutional background. We follow with highlights

and discussion of doctors’, patients’, the press’ and academics’ views of BTD drugs (which we relegate

detailed discussion of to the Internet Appendix). We close with exploration of announcement return

responses to BTD events.

II.A. BTD Institutional Background

In 2012, the US Food and Drug Administration (FDA) created the BTD program to facilitate rapid

approval of therapies that have shown strong results in early trials. Specifically, the designation is awarded

to drugs that are “intended to treat a serious condition and that preliminary clinical evidence indicates

may demonstrate substantial improvement over available therapies” (Sherman et al 2013). While the BTD

program is the fourth addition to the FDA’s expedited approval pathway programs (the other three are:

17 We find no evidence of any difference (post-shock) between rival firms’ and non-rival firms’ proclivity to engage in acquisitions.

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Priority Review, Fast Track and Accelerated Approval), it tops the ranking in terms of where FDA resources

are being prioritized (Senior (2013)).18

Drugs awarded this designation benefit from the organizational commitment of FDA senior

managers, intensive guidance on efficient drug development programs, and from faster approval times.19

Many of these benefits stem from the fact that it is awarded early-on in the drug development process

relative to the other expedited approval pathway programs (Sherman et al 2013). Figure 1 displays an

illustration for the drug development process. Firms typically submit their applications for a BTD with the

investigational new drug (IND) application, and ideally no later than during phase-II of clinical

development. This means that firms awarded the designation can benefit from the program’s features as

early as phase 1 of clinical development, and almost always before the end of phase-II. This expedites the

development process (Sherman et al. (2013)), which has contributed to the program’s popularity.20

Figure 2A displays the distribution of the number of BTDs granted each year since the program’s

inception. The FDA has granted an average of about 35 designation per year. Figure 2B displays the

distribution of the number of BTDs granted in each therapeutic market identified using the first letter of

the corresponding ICD-10 diagnostic code21 (discussed later in section IV.B). Figure 2B shows that the

majority of designations were awarded in the neoplasms and cancers therapeutic markets (119 BTDs of

18 According to the director of the FDA’s Office of Oncology and Hematology, Matthew Herper, the designation “means that the senior management of the FDA division become involved, not just the reviewers who serve on the FDA’s front line.” Further, the “designation means there are more times a company can expect to pick up the phone and get an answer” (Senior 2013). 19 For example, Hwang et al. (2018) find that for a sample of cancer drugs, the median time from IND submission to first FDA approval was 5.2 years relative to 7.1 years for non-BTD drugs, with the difference being statistically significant (p-value=0.01). 20 As of March 2, 2021, the FDA received 1,111 requests for the designation and granted 436. Source: https://www.focr.org/breakthrough-therapies 21 Note that we define a therapeutic market at the 2nd chapter level of the ICD-10 diagnostic codes. However, the first letter of an ICD-10 diagnostic code indicates the general therapeutic area as displayed in Table B1 of Appendix B.

https://www.focr.org/breakthrough-therapies

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the total 253 in our sample).22 This is not surprising since the BTD program is awarded to drugs that are

intended to treat a serious illness, and that cancer drugs account for the majority of all drug development

in the US.23 This finding is also consistent with Puthumana et al. (2018) who find that the most common

approved BTDs were for the treatment of cancer.

Table 1 displays summary statistics on the distribution of BTDs. Our sample consists of 253 unique

BTDs (in section IV.C, we discuss our BTD selection methodology), awarded in 145 ICD-10 markets to 192

drugs and 272 drug-indications. There are 113 BTD firms in our sample of which 83 are public and 30 are

private. Public commercial firms, i.e., public firms with approved drugs on the market, account for the

majority of BTDs granted at 170 (or 67%), whereas public precommercial firms, i.e., public firms with no

products approved for US markets as of the BTD grant date, only account for 57 BTDs (or about 23%).24

This is consistent with the finding in Senior (2013) that most BTDs have been awarded to “large pharma.”

II.B. Patient, Doctor, and Industry Views of BTDs

Demand for pharmaceutical products is driven mostly by prescriptions from physician office

visits.25 In addition, patients may request of physicians specific drug prescriptions, especially for brand

name drugs (Campbell et al. (2013)). This suggests that the demand for pharmaceutical drugs depends to

a large extent on the perception of the best available treatment by both physicians and patients.

22 In Appendix B, which was retrieved from the WHO’s classification, codes “C” and “D” are grouped together. It is noteworthy that most markets with a “D” code are for benign tumors. Therefore, we group “C” and “D” separately. 23 For example, in our comprehensive US drug development data (described later in section IV.A) cancer drugs account for 33% of all drugs developed, followed by drugs intended for endocrine, nutritional and metabolic diseases, which account for about 7%. 24 Note that the same BTD can be awarded to several drugs or several indications of the same drug or to several firms. This is why some subsamples add up to greater than 253. Furthermore, some precommercial firms can become commercial at a later date if one of their products is approved for US markets by the FDA, which results in counting the same firm as both commercial and precommercial if it receives a BTD in both periods, e.g., Clovis Oncology, Acadia, and Spark Therapeutics. 25 For example, the CDC estimates that in 2016, the number of drugs prescribed through physician office visits was about 3 billion units compared to 368.5 million units prescribed at hospital emergency department visits. Source: https://www.cdc.gov/nchs/fastats/drug-use-therapeutic.htm

https://www.cdc.gov/nchs/fastats/drug-use-therapeutic.htm

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Abola and Prasad (2016) find that describing drugs using words such as “breakthrough” creates

public perception that suggests scientific victory and miracle cures. Krishnamurti et al (2015) survey a

random sample of 597 Americans and find that the term “breakthrough” increased people’s belief in a

drug’s effectiveness and “strength of supporting evidence”, and that participants were more likely to

choose such a drug to treat a deadly condition over a drug without such description. This perception is

not limited to the general public as some studies have also found that health professionals and physicians

can also perceive breakthrough drugs as substantially better than existing therapies. For example,

Kesselheim et al (2016) analyze survey data from 692 physicians and find that physicians were more likely

to prescribe the breakthrough drug for their patients than the alternative treatment and conclude that

the choice for the term “breakthrough” may lead physicians to overprescribe the drug.

Collectively, this evidence suggests that physicians (patients) may be more likely to prescribe

(request) a breakthrough designation over alternative treatments, which in turn reduces the demand for

the competing products. For example, an Evaluate Vantage Pharma article discusses how Merck’s

Keytruda has continued to dominate the non-small cell lung cancer therapeutic market, stating that for

Keytruda’s competitors “the boat has sailed, and Keytruda has left them fighting over what is at best a

vanishingly small slice of the pie.”26

The perception of the superiority of BTD drugs held by some patients and physicians is reinforced

by the extraordinary success and effectiveness of some recently approved BTD drugs. For example, Zoulim

et al. (2015) find that Gilead Sciences Hepatitis C BTD drug, Harvoni, cures over 95% of most patient

populations while simultaneously reducing the treatment to 12 weeks. This is a substantial improvement

in Hepatitis C treatment since older treatments required 6-12 months of treatment with cure rates

averaging around 40-45% for genotype1 (Lam et al (2015).

26 Source: Plieth, Jacob. “One More Shot at Slowing Keytruda's First-Line Lung Cancer Domination.” Evaluate.com, 5 Apr. 2019, www.evaluate.com/vantage/articles/news/one-more-shot-slowing-keytrudas-first-line-lung-cancer-domination.

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BTD drugs are also likely to boost the revenues of their manufacturing firms. For example,

Regeneron’s BTD drug for multiple eye diseases Eylea was approved in 2014 for the treatment of diabetic

macular edema. By 2019 it had accounted for a staggering 86% of Regeneron’s total revenues. Merck’s

Keytruda, approved in 2015, accounted for 23% of Merck’s total revenues in 2019. Additionally, in a report

by Evaluate Vantage Pharma, which ranked the drugs approved in 2017 by expected 2022 sales, 7 of the

top 10 drugs were BTDs.27 In addition to the longer-term benefits, BTDs also bring about short-term

financial rewards. For example, Proteostasis Therapeutics witnessed a 70% cumulative abnormal return

(CAR) on the day it announced it was granted a BTD for the treatment of cystic fibrosis.

II.C. Stock Price Reaction Evidence of BTD as a Shock to Future Competition28

To establish that BTD grants are a surprise as well as perceived negatively by rival firms (and their

shareholders), we conduct event studies around the day the BTD was granted.29 We use a market model

with parameters estimated over [−271, −21], relative to the BTD grant announcement date. The abnormal

announcement returns “CAR1” (CAR2) are calculated over the three (five) trading day windows [-1, +1] ([-

2, +2]), where 0 is the BTD announcement date. CARs are winsorized at the 1% and 99% levels. For each

BTD date, we identify the BTD firm, the rival firms, and control firms. BTD firms are those that received

the BTD designation on a given date. Rival firms are firms that have any drug product (fully approved or

under development), that resides in the same ICD-10 market as the focal BTD drug. Control firms are those

that do not have any product that falls in the same ICD-10 market as the BTD drug. This results in

27 These are Ocrevus, Dupixent, Durvalumab, Niraparib, LEE011, KTE-C19, and Ingrezza. Source: Helfand, Carly. “The Top 10 Drug Launches of 2017.” FiercePharma, 30 Jan. 2017, www.fiercepharma.com/special-report/top-10-drug-launches-2017. 28 The Internet Appendix contains additional discussion regarding the validity of BTDs as an exogenous shock. 29 Section IV.C discusses the procedure we use to identify the dates of the BTD grant announcements. In addition to the procedure described in section IV.C, we drop BTDs from the final event studies sample for which we are not able to validate the BTD announcement date. We are left with 247 BTDs.

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identifying 187 BTD firm-dates, 4,898 rival firm-dates, and 72,097 control firm-dates. Note that the same

firm can be a BTD firm on one date and a rival or control firm on another date.

Table 2 reports summary statistics, univariate differences and OLS regressions on these CARs. In

Panel A we report that (on average) BTD firms have a significantly positive CAR1 of 1.7% and CAR2 of 2.2%,

while both rival and control firms have negative and significant CARs.30 We also note that the significant

CARs are mostly driven by precommercial firms as opposed to commercial firms. CAR1 of precommercial

BTD firms equals 4.3% and CAR1 of precommercial rivals equals -0.7%; both of which are significantly

different from the CAR1 of control firms. These results are intuitive and consistent with the findings in

Hoffman et al. (2019) who find that only precommercial BTD firms experience positive and significant

event returns (with magnitude of about 8%).31 Furthermore, these results are consistent with the finding

in Senior (2013) that smaller biotech firms – whose fate may depend on just one or two drug projects –

are more likely to experience a significant change to their valuation upon the announcement of a BTD.

We argue this applies to both BTD firms and rival firms, since smaller biotech rivals who have now received

the bad news that they might potentially compete with a superior product in the same market, can also

experience a negative stock price reaction.

In panel B of Table 2, we run OLS regressions of CAR1 (columns 1-3) and CAR2 (columns 4-6) on

variables that capture a firm’s position in a therapeutic market as well as their drug portfolio overall (using

the full sample). Column 1 confirms that BTD firms have a significantly positive CAR1 comparable to that

reported in panel A, while rival firms have a significantly negative coefficient indicating that CAR1 is lower

for rival firms relative to control firms. In column 3 we include two interactions to capture the change in

CARs for precommercial BTD firms and precommercial rival firms. The BTD firm dummy has a smaller

magnitude where most of the positive effect is driven by the precommercial BTD firms, while the rival

30 CAR1 of around -0.4% and CAR2 of -0.4%. 31 Hoffman et al (2019) have a smaller BTD sample and do not winsorize their CARs. If we do not winsorize our CARs, we find CAR1 equal to about 8% for precommercial BTD firms.

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firm dummy is now insignificant and the interaction of rival and precommercial picks up the compromising

effect of a BTD event on rivals. We include the Firm Total Patents and Firm Total Pipeline variables to

control for firm size. Results are weaker in columns 4-6 arguably because of the wider measurement

window for CARs.

In panel C, we run OLS regressions of CAR1 and CAR2 on separate subsamples of the BTD firms

(columns 1-4) and rival firms (columns 5-8). We again confirm the importance of precommercial status

with significantly positive (negative) coefficients on the dummy for BTD and rival firms respectively.

Moreover, we explore the incremental importance of whether the BTD is part of an original project or

supplemental one. For BTD firms, there is added value to the event when it is part of an original project

for precommercial firms (as opposed to an add-on), by an average of about 8%. However, for rivals (in

columns 5-8), the coefficient on original is insignificant. The announcement of a BTD alone is bad news for

rivals, especially precommercial rivals, whether this BTD is original or supplemental.

Overall, the Table 2 results indicate BTDs carry important valuation implications. BTD firms are

better off especially when the firm is precommercial. Rivals are worse off, again especially precommercial

ones. The results also suggest that the BTDs are a surprise to the market, and thereby may be viewed as

exogenous.

Finally, since BTDs are granted earlier in the development process, we discuss why BTDs are still

credible threats to future competition even though there is a chance they are not approved. First, the

average approval rate for BTD drug indications in our sample is 46%32 (117/253), whereas in the full

sample of all drugs, it is about 5% (1459/26596). Second, in untabulated results, we randomly match our

sample of BTD drugs to similar control drugs in the same ICD-10 (i.e., therapeutic) market, and having the

same patent status, same initial development status reported, and similar drug age. We use this matched

32 This number is comparable to that reported on the FOCR website for all BTDs which is equal to 46.7% (204/436). In addition, many BTDs in our sample are awarded in 2017 and onwards and have therefore not yet received FDA approval since approval takes an average 2.5 years from phase-II development to FDA approval.

15

sample to run a hazard model on the likelihood of receiving FDA approval and find that BTD drugs are 3.5

times more likely to be approved relative to non-BTD drugs. Third, we run a separate logit model using

this randomly matched sample while accounting for the right censoring issue of our data by dropping drug

projects that started development after 2017Q4 and find similar results.33 Finally, the literature provides

support for our claims that BTD drugs are more likely than the average drug to receive FDA approval (see

for example Hermosilla (2020)) and that they receive FDA approval in a shorter time (see Hwang et al

(2018)).

Overall, this section has made the case that BTDs are exogenous shocks to future competition as

follows. First, BTDs are perceived as superior drugs by the public, physicians are more likely to prescribe

them to patients, and patients are more likely to request them from physicians. Second, we reviewed

anecdotal evidence that demonstrates how BTDs dominate the therapeutic markets they operate in.

Third, announcement returns indicate that BTD firms (especially precommercial) benefit, while rival firms

(especially precommercial) suffer from such designations. Finally, BTDs are more likely to be approved

(and earlier) relative to non-BTDs.

III. Hypotheses Development

Our hypotheses emerge from the theoretical model outlined in Aghion et al. (2005), with

adaptations to reflect the specifics of our biopharmaceutical industry focus. Aghion et al. posit duopoly

industries with a leader possessing superior technology, and a laggard. When the two firms in an industry

are at technological par with each other, the industry is leveled (neck-and-neck); otherwise, it is

unleveled.34 Profit varies with the technology gap between leader and laggard in unleveled industries, and

33 We also examine the CARs of BTD firms and rival firms on the day the BTD drug is approved. Results are similar in sign but smaller in magnitude with weaker statistical significance for BTD firms and no statistical significance for rival firms. This corroborates the importance of BTD announcement as an event. 34 The model allows either type of firm (leader or laggard) to innovate.

16

by the extent to which the two firms in a level industry collude. In other words, profits reflect both the

state of industry competitiveness as well as firm status (as leader or laggard). Finally, laggards may imitate

the leader’s innovation to catch up, with more research intensity (i.e., innovation) raising the hazard rate

on catch up.

Their Proposition 1 underlines the varying effects of competition on innovation. It first shows that

research intensity (innovation) increases in industry competition level, when the industry is in a level state.

They refer to this result as the “escape-competition effect.” When competitors are neck-and-neck,

innovating allows one firm to get ahead and reap profits which are increasing in the competition variable.

The underlying intuition is presented through discrimination of pre-innovation rents from post-innovation

rents. When a product market is highly competitive, rents are low. The escape competition incentive to

innovate is driven by higher rents to being separated post-innovation than to being pooled pre-innovation.

Proposition 1 follows with the unleveled state result; research intensity (innovation) of the

laggard firm decreases with competition. They refer to this as the Schumpeterian effect. Again, in the

context of post-innovation vs. pre-innovation rents, laggards see less payoff to catching up through

innovation when higher competition reduces such post-innovation rents.

Aghion et al. explain the inverted-U shape relationship between competition and innovation as

follows. The extent of competition in a product market (i.e., industry or therapeutic area in our study),

influences the tendency to remain in a leveled or unleveled state. They refer to this as the composition

effect. When competition is ex-ante low, there is little incentive for neck-and-neck firms to innovate and

therefore the industry tends to stay leveled. But this further implies that a shock to competition

encourages a quick return to leveling as the follower firm innovates to catch up. This last sentence implies

our two pieces of Hypothesis 1.

Hypothesis 1a: Ex-ante low competition therapeutic markets will respond to competitive shocks (BTDs) with faster innovation on average.

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Hypothesis 1b: The average effect in 1a will be driven by followers (laggards) in that market. The acceleration of innovation in response to BTD shocks will be concentrated in the follower sub-sample. The other “side” of the inverted-U is found in more competitive industries. These tend to stay in

an unleveled state for two reasons. There is little incentive for laggards to catch up.35 And if the industry

levels, the high competition encourages quick innovation to escape such competition. Put differently,

leaders wish to stay leaders in highly competitive markets. They only innovate when they are caught by

laggards (i.e., they become leveled) and then they are quick to do so. But the average effect is less

innovation since laggards have low incentive to innovate in the first place, which would be the only way

to level the industry. Overall, Aghion et al. predict more competitive markets will see less innovation in

response to competitive shocks, and this is driven by laggard reticence to innovate in such markets.

Hypothesis 2a: Ex-ante high competition therapeutic markets will respond to competitive shocks (BTDs) with slower innovation on average. Hypothesis 2b: The average effect in 2a will be driven by followers (laggards) in that market. The deceleration of innovation in response to BTD shocks will be concentrated in the follower sub-sample. As we discuss in section V, our Hypotheses 1 and 2 concentrate on phase-II projects and their

continuation (or not) to phase-III. This is for two reasons. The decision to continue to phase-III involves

significant resources. This aligns with Aghion et al.’s model where R&D expenditures positively associate

with the hazard rate of (the follower) moving ahead (to catch up). Second, it preserves the within-industry

perspective underlying their model, because we treat each therapeutic market as a unique industry.

However, the fixation on phase-II to phase-III continuation comes at a cost. These drug projects

are further along the development chain than either new drug discovery or pre-clinical or even phase-I

clinical trial projects. In short, they are not new and so may be viewed as less than truly innovative. We

therefore develop Hypothesis 3 around new drug discovery stage projects. Here we fully admit the

potential for two different therapeutic markets (i.e., industries) to be in play; the shocked (by BTD) market

35 Recall that post-innovation rents are low in more competitive industries.

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and the new drug discovery project market. Since ex-ante market competitiveness influences innovation

incentives (in response to shocks), the potentially different market(s) competitive stature(s) must be

recognized. We therefore focus on relative competitiveness of the shocked (old) vs. new (drug discovery

project) market in this analysis.

Under Aghion et al., followers in the shocked market are expected to accelerate (decelerate)

innovation when competition is low (high). Put differently, they avoid investing where post-innovation

rents are comparatively lower. We therefore posit that a follower shocked by a BTD is expected to place

a new drug discovery project – i.e., the innovation – in a market that is less competitive than the old

(shocked) one.

Hypothesis 3: Laggard firms’ new drug discovery projects that are started in response to BTD shocks will tend to be located in less competitive markets (than the shocked one).

IV. Data and Variable Construction

In this section, we first discuss our drug development data source and procedures for identifying

drug manufacturers. We follow with discussion of our therapeutic markets and drug technology

identifications. Third, we present the process for finding BTDs. We conclude with discussion of calculating

therapeutic market competition, defining leader and follower firms, and identifying rival firms.

IV.A. Drug Development and Manufacturer Data

We obtain comprehensive drug development records from Cortellis Competitive Intelligence™.

Cortellis is an industry competitive repository of pharmaceutical innovation that obtains information from

company records, conferences, and other public sources, and has been used by several papers in

economics research (e.g., Krieger 2021, Krieger et al. 2021, Hermosilla 2018). The full sample includes

development histories on over 13,000 drugs and 30,000 drug-indications developed by over 5,000 firms

and updated until 2020q2. Cortellis provides information on the following fields that we use: drug names,

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drug-indications (i.e. the medical condition that the drug is intended to treat), drug originating firm and

drug current and previous owners, drug sales in 2018 (for FDA approved i.e. launched drugs), drug target-

action (i.e. drug technology), drug regulatory designations (e.g. breakthrough designation and priority

review designation), information on patents covering the drug, a detailed history of key drug development

events and dates, and an extract containing a detailed description of the drug development.

We keep only drug-indications developed for US markets. Furthermore, we drop drug-indications

with missing key development dates. We identify the issue and expiration dates of drug patents by using

the patent data in Cortellis36. We construct a quarterly panel of data for each drug-indication reflecting

the development stage the drug was in during that quarter. Consistent with Li et al. (2020), we drop

“zombie” projects after they are suspended.37

Our sample begins in 2010q1 because we wish to have approximately three years of data before

the first approved BTD (which was in late 2012). We identify the developing firm for each drug-indication

from Coretllis Data. It lists the originator firm for each drug as well as the firms that are actively developing

the drug and the firms that previously developed but are no longer developing the drug. Cortellis’

“Extract” field also contains elaborate information on the ownership of the drug, and whether the

originating firm was acquired or is a subsidiary of another firm, or whether the firm changed its name.

Cortellis, however, does not list the date of the ownership change. To match each drug-indication to its

correct owner in each quarter we use SDC Platinum, Informa’s publicly available Scrip website, Bloomberg

terminal information, as well as popular business media searches; and we follow the matching procedure

explained in Appendix C. The resulting sample includes 12,769 drugs developed for 29,672 drug-

36 Cortellis provides information on the patent number, indication the patent was awarded for, the patent owner, patent grant date and patent expiration date. We create a dummy variable, patent, equal to one if the drug project is covered by a patent in a given quarter, and zero otherwise. 37 Firms are often reluctant to report project suspensions. Consistent with Li et al (2020), we assume “zombie” projects are suspended 3 years after a “no development reported” designation in the Cortellis data.

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indications by 4,392 firms. Given the 41 quarters from 2010q1 to 2020q1, this implies 566,303 drug-

indication-quarter observations.38

IV.B. ICD-10 Therapeutic Markets

A therapeutic market (indication) is the medical condition that a drug is meant to treat. A single

drug may be developed for several indications. Approximately 35% of drugs in our data are developed for

more than one indication. Cortellis reports the indication for which a drug is intended to treat, e.g.,

“Metastatic Breast Cancer.” In some cases, two or more indications are actually referring to the same

condition, e.g., the indication “liver disease” is likely the same indication as “liver cirrhosis” (Krieger 2021).

To identify potentially competing products within a therapeutic market, we map Cortellis indications to

the 10th revision of the International Statistical Classification of Diseases and Related Health Problems

classifications (ICD-10).39 Finally, we group indications at the second subchapter level.40 We do so because

we want to ensure that drug-projects within the same therapeutic area are indeed addressing the same

medical condition. For example, while they are two different markets with different market characteristics

and players, both “Non-small Lung Cancer” (ICD-10 = C34-90) and “Small Lung Cancer” (ICD-10 = C34-91)

have a first subchapter ICD code of C34.41 Our final set of unique ICD-10 therapeutic markets numbers

1,308.

38 Note that several firms can develop the same drug-indication. In those cases, we consider each drug-indication-firm as a separate project. In untabulated robustness tests, our main conclusions continue to hold if we instead only assign the drug project to the largest firm with the highest number of developed projects to date (see Krieger (2021)). 39 We consult a clinical pharmacist to find the concordance between the Cortellis indication names and the ICD-10 diagnostic codes at the second subchapter level (e.g., stage IV Melanoma has an ICD-10 code of C43-9). Note that some indications only have an ICD-10 code at the first subchapter level, e.g., essential hypertension has an ICD-10 code of I10. When this is the case, we use the first subchapter designation (instead of deleting the observations). 40 For example, the indications “Non-Small Cell Lung Cancer” and “Metastatic Non-Small Cell Lung Cancer” are both assigned the ICD-10 code of “C34-90.” 41 In untabulated robustness tests, we use strictly the first ICD-10 subchapter to define a market, and obtain similar results, albeit weaker statistically. We also obtain similar results using the ICD9 diagnostic codes which were generously shared by Manuel Hermosilla.

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IV.C. BTD Designations

To identify the BTD designations and grant dates, we use the Friends of Cancer Research (FOCR)

website42 which identifies each BTD drug name, the announcement date, the sponsoring firm and the

indications for which the BTD was granted. In addition, we use the “Regulatory Designation” field in

Cortellis which also identifies when a BTD was granted but does not identify the grant date or the drug-

indication.43 If a BTD is granted to more than one drug, or more than one firm, we treat each as a separate

BTD. We validate announcement dates by cross-checking with firm financial statements, FDA disclosures

and business media articles. We also cross check our dates with the 143 BTDs in the online supplementary

appendix for Hoffman et al. (2019). We identify whether a BTD is original or supplemental using the FDA’s

CDER and CBER BTD approval lists.44 Finally, we drop (5) BTDs that were rescinded from the sample. We

are left with 253 BTDs. Table 1 provides descriptive statistics on BTD designations in our sample.

IV.D. Competition, Leaders, Followers and Rivals

We define competition for each market-quarter as the number of drug projects in an ICD-10

therapeutic market in any stage of development in a given quarter. Our competition measure is similar to

those used in Cunningham et al. (2021) and Hammoudeh and Nain (2019). We also replicate our main

analyses using the number of firms who are actively developing drug projects in a given ICD-10 market for

a given quarter and report the results in Appendix E. This latter competition measure is similar to the one

used in Krieger (2021).

We define leaders as firms that have a product that has been approved by the FDA for a given

ICD-10 market, and followers as firms that are actively developing drug projects for a given ICD-10 market

42 https://www.focr.org/breakthrough-therapies 43 We manually match the FOCR data to our data by drug name using the procedure described in Appendix D. 44 Note that this list contains BTDs that were approved. For BTDs that are not yet approved we search firm and media disclosures to identify whether a BTD is original or supplemental. For BTDs that are still not defined, we assume that if the BTD was jointly awarded to two firms, or if the same drug had previously received a BTD then it is supplemental, otherwise, it is original.

https://www.focr.org/breakthrough-therapies

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but have not yet received FDA approval.45 Note that the same firm can be a leader in one ICD-10 market

and a follower in another. At the firm level, we define rival firms as firms who are actively developing drug

projects in an ICD-10 market that has experienced BTD entry and that are not the BTD firm.

V. Empirical Design and Results

In this section, we first examine the likelihood of continuing phase-II development analyses. We

follow with exploration of drug initiations by rival and non-rival firms.

V.A. Phase-II to Phase-III Development Continuation – The Preliminaries

To test our hypotheses 1 and 2, we construct a panel of all phase-II projects in our sample. We

focus on phase-II projects for a couple of reasons (mirroring Krieger (2021)). First, this is the initial test of

a drug’s efficacy in humans, requiring significant capital investments.46 Second, they have much higher

levels of uncertainty relative to phase-III projects. Hay et al. (2014) report that 16% of phase-II projects

are eventually approved relative to 50% approval rate of phase-III.47

We then identify any drug projects that reside in the same ICD-10 market that experienced BTD

entry. We set the dummy variable “Mkt Shock” equal to one if the drug project resides in a BTD-shocked

market, in all quarters equal to and greater than the quarter of the BTD grant; zero otherwise. We define

45 Note that some ICD-10 markets do not have any products approved by any firms. In those cases, leaders are firms with the drug projects that are furthest along in clinical development. For example, as of May 2021, no firm had received full FDA approval on a COVID-19 vaccine. Therefore, leaders in this therapeutic market were Moderna, Pfizer, J&J, and Astra-Zeneca, all of whom had been granted EUA. In the absence of such EUA or full FDA approval, ongoing phase-III clinical trials would be coded as leadership. 46 On average, phase-II projects cost between $13 million and $80 million, whereas phase 1 projects cost between $4 million to $8 million (Krieger 2021). 47 We drop phase-II projects of BTD firms in the same ICD-10 markets as the BTD was granted. Furthermore, phase-II projects of the BTD drug are dropped for the indications that reside in the same ICD-10 market as the BTD indication (since the same drug can be developed for several indications, and several indications can fall in the same ICD-10 market, for example the indications “Non-Small Cell Lung Cancer” and “Metastatic Non-Small Cell Lung Cancer” are both assigned the ICD-10 code of “C34-90.”)

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our dependent variable Development Dummy equal to one in the quarter that a drug project reaches

phase-III clinical trials, and zero if it remains in phase-II.

Table 3 presents summary statistics for the phase-II drug projects sample. We have 4,901 phase-

II drug projects developed by 1,179 firms in 748 ICD-10 markets from 2010q1 to 2020q2. The presentation

is also partitioned into rival projects, i.e., projects that reside in a market that experiences BTD entry

(column 2 “Market Shock”), and control projects, i.e., projects that reside in a market that has not

experienced BTD entry (column 3 “No Market Shock”).48

Several observations are worth noting. First, drug projects in ICD-10 markets without BTD entry

are more likely to report a development to phase-III relative to drug projects in BTD shocked markets.

Absent controls or timing considerations, it appears that BTDs discourage rivals from continuing

development of phase-II projects. Second, markets that experience BTD entry contain a higher proportion

of follower firms, have higher competition and higher market growth, relative to markets that do not

experience BTD entry. Finally, firms that operate in markets that experience BTD entry have fewer

approved products on the market, more patents covering drug projects and more total firm projects,

relative to firms operating in ICD-10 markets that haven’t experienced BTD entry.49 Our multivariate tests

must necessarily control for all of these factors. Nevertheless, we also address concerns that these

differences imply endogenous factors may be driving our results: when we exclude cancer therapies in

our robustness checks, the above-noted ex-ante differences disappear. Those robustness checks continue

to imply our main inferences.

We offer pictorial relationship explorations (across subsamples) in Figure 3. The bar graphs

measure the probability of continuing phase-II development simplistically, as the number of projects (in

the subsample) that reach phase-III development, divided by all phase-II projects in that subsample.

48 All variables are defined in Appendix A. 49 Note that the same firm can experience BTD entry in some markets and not in others. The Firm-Mkt variables vary for the same firm across different markets.

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Figure 3A shows that drug projects which reside in un-shocked markets are more likely to reach

phase-III development, mirroring the Table 3 result. Figure 3B shows leader firms are more likely to

continue development than follower firms. Figure 3C shows that drug projects which reside in ICD-10

markets with higher competition are less likely to continue development.

We next turn to parametric estimation of continuation likelihood. Our choice for model

specification must account for the following issues. First, the outcome variable is binary, i.e., firms

continue development of a phase-II project and reach phase-III, or they do not. Second, the response

varies in time such that firms can continue development at any point before the end of the sample period.

Finally, there is right censoring of our data, since drug development typically lasts several years, and firms

may eventually continue development after the end of our sample period.

These issues motivate our choice of a hazard model, which accounts for right censoring as well as

the temporal nature of our data by explicitly controlling for the spell length (time until response), and

allows the outcome variable to be binary. Furthermore, this model has been used in other papers for

similar purposes (e.g., Krieger (2021), Aboulnasr et al. (2008)). We use the Cox proportional-hazards

model with the Development Dummy as our success event.50 The analysis time is the number of quarters

since the start of phase-II development.

Our identification assumption is that BTD entry is an exogenous shock to future competition. To

test this assumption, we need a difference-in-differences test that shows the following: phase-II projects

in ICD-10 markets that eventually experience BTD entry should be just as likely to reach phase-III, as phase-

II projects in non-shocked ICD-10 markets before BTD entry (i.e., parallel trends). We therefore create a

50 We build our empirical tests around drug developments, rather than drug discontinuation for two reasons. First, many firms are reluctant to officially disclose the exact date of development discontinuation, leading to significant measurement error, whereas development continuation dates are usually reported in a timely manner. Second, firms discontinue drug projects for reasons not motivated by strategy and competition. For example, firms may discontinue the development of projects that have shown adverse effects in clinical trials (See Hermosilla (2018) for other examples).

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dummy variable, eventualBTD, equal to one if the drug project resides in an ICD-10 market that eventually

experiences BTD entry, and zero otherwise. We run a hazard model on the likelihood of phase-II

development using the interaction of eventualBTD with indicator variables for each of the 3 years before

and up to 5 after BTD entry. We select 3 years as the window before BTD entry because the first BTD

award in our sample occurs in the last quarter of 2012 and the sample starts in the first quarter of 2010.51

Figure 5 displays the coefficients from this analysis. They show that in the 3 years before BTD

entry, the likelihood of reaching phase-III for projects that eventually experience BTD entry is not

statistically different from projects that never experience BTD entry. The difference starts to significantly

differ after the first year of BTD entry where projects in BTD shocked markets are about 52% as likely to

reach phase-III relative to projects that never experience BTD entry. In the second, third, fourth and fifth

years after BTD entry, projects are 63%, 44%, 32% and 37% as likely to continue phase-II development as

projects in un-shocked markets, respectively.52 This supports our identification assumption that BTD entry

is an exogenous shock to future competition.

Figure 6 then offers plots of the cumulative hazard function (CHF) for phase-II development

projects, across varying samples. These illustrations provide two important perspectives. They show the

importance of BTD shocks to continuation hazards; and they begin to provide a view of the interacting

influence of ex-ante competition within a market and the firm’s position (leader vs. follower) on the same.

We begin with an overview in Figure 6A, showing the CHF function for all phase-II projects in the

sample. The likelihood of approval increases with project age. Figure 6B then illustrates how BTD shocks

affect continuation: drug projects in BTD shocked markets are much less likely to reach phase-III, relative

to drug projects that reside in un-shocked markets. Figure 6C begins the tie-in to Aghion et al. (2005). It

shows that projects developed by follower firms are much less likely to be developed than projects

51 We get similar results if we instead use the 5 years before BTD entry as the window. 52 The coefficient on the interaction between the eventualBTD and the second year is statistically insignificant.

26

developed by leader firms. And Figure 6D shows that projects in less competitive markets are more likely

to continue to phase-III.

Finally, Figure 7 illustrates the non-linear relationship between competition and innovation at the

therapeutic product-market (i.e. industry) aggregation level. We build the figure as follows. We first

average the competition level across all periods, separately for each therapeutic market. We then

calculate the percentage of phase-II projects within each therapeutic market that advance to phase-III,

again across the full sample period (but calculated separately within each therapeutic market). The

percentage variable becomes our proxy for innovation. Finally, we use a fractional polynomial model to

plot the “aggregated up” innovation measure against the average competition level across therapeutic

markets.53 The figure shows that the likelihood of continuing phase-II development increases until there

are about 20 other competing projects in a therapeutic market (i.e. industry), after which it starts to

decline, forming an approximate inverted-U pattern.54

V.B. Tests of Aghion et al. (2005)

This section formally tests hypotheses 1 and 2. Our goal is to examine whether a competitive

shock will increase or decrease innovation within a product market, and whether the effect varies with

ex-ante market competitiveness and the firm’s position in it. Table 4 reports the phase-II development (to

phase-III) hazards using the full sample, and then using subsamples partitioned on the level of market

competition. Full sample results are presented in columns 1 and 2.

In column 1, the coefficient on Mkt Shock is -0.63 and significant at the 1% level. Absent any

competition controls, a BTD shock within a therapeutic market decreases the likelihood that a phase-II

53 We select the fractional polynomial model because it allows more flexibility for the parameterization of continuous variables (relative to regular polynomial models). It offers incremental advantages to using the standard quadratic model through its ability to illustrate both the curvature and the skewness of this relationship (as seen in Figure 7). 54 For readers interested in a more traditional quadratic framework, but using project-level data, see Appendix E, Table E1. There we offer a Cox proportional-hazards model with competition and competition squared as independent variables. Phase-II to phase-III continuation (i.e. innovation) is positively associated with competition and negatively associated with competition squared, consistent with the findings in Aghion et al.

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project advances to phase-III by about 47% relative to phase-II projects in non-shocked markets. Column

2 suggests that this decrease in likelihood is driven by therapeutic areas with ex-ante high levels of

competition. The coefficient on Mkt Shock interacted with Competition is -0.498 and significant at the 1%

level; a one standard deviation increase in the ex-ante level of competition decreases the likelihood of

phase-II development by (1-exp(1.45*-0.498)) about 50%. The control variables in column 2 indicate that

firms are more likely to continue phase-II development and reach phase-III if they own more launched

products within a product market, and if they are publicly listed. Furthermore, firms with more drug

projects in total are slightly less likely to continue phase-II development to phase-III.

Columns 3 through 6 of Table 4 more directly link our phase-II development hazard results to the

inverted-U shaped relationship between competition and innovation of Aghion et al (2005). Each column

provides phase-II development hazards using quartile subsamples sorted on the level of ex-ante product

market competition. In column 3, the coefficient of Mkt Shock indicates that in ex-ante low competition

markets, phase-II projects that experience BTD entry are almost twice as likely to reach phase-III relative

to phase-II projects that do not experience BTD entry. This result confirms our hypothesis 1a and is also

consistent with the implications of Aghion et al. (who predict that in ex-ante low competition markets,

innovation is increasing in product market competition). In column 6, our hypothesis 2a is confirmed:

shocked phase-II projects that reside in the most competitive therapeutic product markets are about 57%

less likely to continue development relative to non-shocked phase-II projects (in these markets). This

aligns with Aghion et al.’s (2005) characterization of high competition markets where innovation is

decreasing in product market competition. It’s also worth noting is that the test of proportional hazards

assumption is satisfied (i.e. the tests yield insignificant results) in all columns of Table 4 (except for column

4), implying that the grouping in our tests forms proportional samples and that inferences from a

proportional hazards model are valid.

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We next examine whether the impact of competitive shocks on innovation observed in Table 4

vary by a firm’s competitive position within a product market. The ability to test this using granular

project-level data within any individual firm is a key benefit of the focus on pharma. In the context of

Aghion et al. (2005), we test whether rival response to BTD entry is driven by follower firms. We run a

hazard model similar to that in Table 4 but partition the sample by the firm’s competitive position (leader

or follower) and report the results in Table 5.

The first 3 columns of Table 5 use the subsample of follower firms while the last 3 columns use

the subsample of leader firms.55 We include all follower firms in column 1 and interact the Mkt Shock

variable with Competition. The coefficient on the interactive variable indicates that a one standard

deviation increase in market competition decreases the likelihood of advancing to phase-III by about 65%.

This suggests that the interaction effect (of competition) in column 2 of Table 4 may be driven by follower

firms.

We examine this further by dividing the subsample of follower firms into low (bottom quartile)

and high (top quartile) market competition groups, eschewing the middle two quartiles of

competitiveness (similar to Krieger (2021)). The coefficient on Mkt Shock in column 2 implies that when

ex-ante market competition is low, shocked phase-II projects of follower firms are almost 350% more

likely to reach phase-III relative to non-shocked phase-II projects of follower firms. This result confirms

our hypothesis 1b and is consistent with the implications of the Aghion et al. model. Specifically, when ex-

ante competition is low an industry is quick to leave the unleveled state and that this behavior is driven

by laggard behavior. Follower firms have an incentive to catch up with leader firms in order to realize

post-innovation profits in less competitive markets.

55 The variable Firm-Mkt Launch is omitted in the follower subsamples in Table 5 since by definition, follower firms do not own any launched products in a given market.

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On the other hand, when ex-ante competition is high – as in column 3 – follower firms are 60%

less likely to continue development of shocked phase-II projects relative to non-shocked phase-II projects.

This supports hypothesis 2b. It is consistent with the prediction that follower firms have little incentive to

innovate after competitive shocks in ex-ante high competition markets, since post-innovation profits are

low in such markets.

Turning to analysis of leader firms, we see little influence of the BTD (competition) shock on

continuation of phase-II projects to phase-III (i.e. innovation). Nor does segmenting by ex-ante

competitiveness of the therapeutic (i.e. product) market mediate the effect. Taken together, the results

in Table 5 suggest that the effect of BTD entry on innovation observed in the full sample in Table 4 is

driven by follower firms as predicted in hypotheses 1b and 2b.

Figure 4 illustrates two BTD entry examples to help provide clarity on the results from Table 5.

Figure 4A presents Catalyst Pharmaceutical receiving BTD designation for Firdapse in the ICD-10 market

G70-80, which is an ex-ante low competition market. Post-shock, one third of the follower firms continue

development to reach phase-III. By contrast, Figure 4B shows the example of Merck and Eisai both

receiving BTD designation for their combo therapy of Lenvima and Keytruda in the ICD-10 market C54-1,

which is an ex-ante high competition market. All followers (22 firms) do not report any developments.

To further validate our findings in Table 4, we replicate the analysis using a linear probability

model (LPM) and a fixed effects logit and report the results in Table E2 of Appendix E.56 We account for

the right censoring nature of our data by dropping phase two projects initiated after 2017q4.57 The results

in Table E2 are generally consistent with those reported in Table 4, albeit with weaker statistical

significance possibly due to the hazard’s ability to explicitly control for the timing of a rival’s response.

56 The LPM model includes firm and quarter fixed effects and clusters standard errors by firm. The LPM results are similar if we include therapeutic market fixed effects and if we cluster standard errors by therapeutic market. The fixed effects logit model uses robust standard errors and includes firm fixed effects. 57 The average time that a phase-II project takes before reaching phase-III is two years in our data.

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Additionally, we replicate the results in Table 5 using similar LPM and fixed effects logit models and report

those results in Table E3 of Appendix E. Similar to Table E2 we account for right censoring, and find results

similar to those reported in Table 5.

V.C. Drug Project Initiations

Given the non-trivial progress that phase-II drug projects represent, we explore drug initiation

activity as an alternative response to BTD shocks. We attempt to answer two main questions: do rival

firms initiate new drug projects in response to BTD entry into one of their existing-projects’ therapeutic

markets? Second, are such new drug projects “placed” with competitive pressures in mind? To answer

these two questions, we search for drug initiations in Cortellis when a firm reports a “discovery”

development stage.58 We identify the date the drug project was initiated, the initiating firm and the

therapeutic market in which the project was initiated. We identify a total of 7,851 drug initiations by 2,424

firms in 935 therapeutic markets from 2010q1 to 2020q1.

V.C.1 Likelihood of Firm Drug Initiations

The first question requires us to use firm-level data to determine whether a rival is more likely

than a control firm to initiate a drug project shortly after being shocked. We construct a panel of firm

quarters that starts in 2010q1 and ends in 2020q1.59 The final panel includes 4,134 firms over 41 quarters

and corresponds to 83,623 firm-quarters. The dependent variable, Initiation Dummy, equals one if the

firm initiates a drug project in a given quarter and zero in all other quarters. The main independent

variable, Mkt Shock Count 8 (4) Quarters, counts the number therapeutic markets that the rival

experienced BTD entry within, during the last 8 (4) quarters.60 We calculate the market level variables,

58 Special thanks to Dennis Erb for guidance on this choice. Discovery stage is when a firm ‘targets’ a therapeutic area with a drug project. 59 To reduce the confounding effects of receiving a BTD award on drug initiations, we drop firms that are awarded a BTD from our panel, i.e., we ensure the panel contains only rival and control firms. 60 If we instead replace the Mkt Shock Count with a Mkt Shock Dummy that takes on a zero or one value, we get similar results to those reported in Tables 7 and 8.

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Competition and Market Growth, at the firm level for each quarter, by taking the weighted-average value

of these variables across all the therapeutic markets that a firm operates in. The weights are assigned by

the number of active drug projects for a firm in a market-quarter. Finally, we include two firm-level

variables, Firm Total Patents and Firm Total Projects, to proxy for firm innovativeness and firm size,

respectively.

Panel A of Table 6 provides summary statistics describing the firm level sample. First, an average

of 5.4% of the full sample of firm-quarters experience a drug initiation. Second, the Mkt Shock Count

variable appears to be left skewed; 26% (20%) of the sample have experienced BTD entry in at least one

market in the last 8 (4) quarters, and 9% (6%) experienced BTD entry in at least two markets in the last 8

(4) quarters. About 10% of firms have at least one patent, and about half of sample firms have two or

more drug projects in total.

We address the first question – do rival firms initiate new drug projects in response to BTD entry

– in Table 7. We use both an LPM and fixed effect logit to run the following regression model

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑞𝑞 = 𝐵𝐵1𝑀𝑀𝑀𝑀𝐼𝐼 𝑆𝑆ℎ𝑜𝑜𝑜𝑜𝑀𝑀 𝐶𝐶𝑜𝑜𝐷𝐷𝐼𝐼𝐼𝐼𝑖𝑖,𝑞𝑞−1 + 𝐵𝐵2𝐶𝐶𝑜𝑜𝐷𝐷𝐶𝐶𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑜𝑜𝐼𝐼𝑖𝑖,𝑞𝑞 +

𝐵𝐵3𝑀𝑀𝐼𝐼𝑀𝑀𝑀𝑀𝐼𝐼𝐼𝐼 𝐺𝐺𝑀𝑀𝑜𝑜𝐺𝐺𝐼𝐼ℎ𝑖𝑖,𝑞𝑞 + 𝐵𝐵4𝐹𝐹𝐼𝐼𝑀𝑀𝐷𝐷 𝑇𝑇𝑜𝑜𝐼𝐼𝐼𝐼𝑇𝑇 𝑃𝑃𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑃𝑃𝑖𝑖,𝑞𝑞 + 𝐵𝐵5𝐹𝐹𝐼𝐼𝑀𝑀𝐷𝐷 𝑇𝑇𝑜𝑜𝐼𝐼𝐼𝐼𝑇𝑇 𝑃𝑃𝑀𝑀𝑜𝑜𝑃𝑃𝐼𝐼𝑜𝑜𝐼𝐼𝑃𝑃𝑖𝑖,𝑞𝑞 + 𝐹𝐹𝐼𝐼𝑀𝑀𝐷𝐷 𝐹𝐹𝐹𝐹 +

𝑄𝑄𝐷𝐷𝐼𝐼𝑀𝑀𝐼𝐼𝐼𝐼𝑀𝑀 𝐹𝐹𝐹𝐹 + 𝜀𝜀𝑖𝑖,𝑞𝑞 (1)

where i and q are subscripts indicating firm and quarter, respectively. Standard errors are

clustered at the firm level. The LPM facilitates economic interpretation relative to non-linear models (e.g.,

logit) and allows for the inclusion of firm and quarter fixed effects that help control for differences in (for

example) firm initiation activity, and initiation activity across quarters. The logit model is useful in

situations where the true probabilities are extreme (Long 1997), i.e., the dependent variable averages

close to one or zero. Since the average value of the dependent variable is 5.4%, we report the fixed effects

logit results with robust standard errors and firm fixed effects.61

61 Quarter fixed effects are suppressed in the FE logit because otherwise we fail to achieve convergence.

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Table 7 displays the LPM (columns 1 and 3) and FE logit (columns 2 and 4) regression results using

the model in equation 1. The first (last) two columns use the Mkt Shock Count variable that counts the

number of shocked therapeutic markets for a firm in the last 8 (4) quarters. Column 1 (3) suggests that a

one standard deviation increase in the number of shocked markets in the last 8 (4) quarters increases the

likelihood that a rival initiates a drug project by 0.45% (0.5%). Thus, recently shocked rivals appear slightly

more likely to initiate drug projects relative to control firms. In addition, firms that are facing more

competition are less likely to initiate drug projects, while firms that operate in growing product markets

and firms with larger drug portfolios are more likely to initiate projects.

We investigate firm drug initiation likelihood further by segmenting the analysis by quartiles of

firm-wide competition levels. Table 8 reports the results from LPM (columns 1, 3, 5, and 7) and fixed

effects logit (columns 2, 4, 6, 8) models using equation 1, but excluding the competition variable because

of our sub-sampling. In panel A of Table 8, the first (last) four columns use the first (fourth) quartiles of

competition, while panel B of Table 8 reports the results using the second (third) quartile subsample in

the first (last) four columns. The results indicate that firms facing lower levels of competition in the

shocked market (the first and second quartiles of competition), are significantly less likely to initiate drug

projects. By contrast, firms that are shocked on projects which reside in ex-ante more competitive markets

are more likely to initiate new drug projects.

V.C.2. Rival Drug Initiation Locations Based on Market Competition

We suspect that the tendency to initiate a new project when shocked on a product that resides

in ex-ante more competitive markets, is driven by follower firms attempting to locate their new (potential)

product in less competitive markets with potentially higher post-innovation rents. This was formalized in

our hypothesis 3. To test this we need to construct a firm-market panel that allows us to identify followers

and leaders, as well as the level of competition in each of the shocked market and the initiation market.

We specifically create a cross-sectional sample – at the firm-market level – of all drug discovery project

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initiations that happened within 8 quarters of the BTD shock (experienced by any rival with a project in

that shocked area). In other words, all firm-markets included were shocked in the last 8 quarters, and all

quarters included must be quarters in which firms initiated a drug project.

Rivals shocked in the last 8 quarters initiated 2,436 drug projects. We match these initiations to

all the firm-market observations in which the firm was shocked in quarter q-1 (i.e. the firm-market must

be shocked one quarter before the drug initiation quarter). Note that the same drug initiation can be

matched to several firm-market observations.62 The final sample includes 23,504 observations for 914

firms in 603 shocked markets and 132 initiations markets,63 from 2013q1 (since the first BTD was awarded

in 2012q4) to 2020q1.

We require a measure of relative competition between the therapeutic markets where the new

drug discovery project is located and where the shocked project was located, in order to test hypothesis

3. We calculate our analysis dependent variable, Change in Competition Ratio (hereafter CCR), as follows:

𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝐴𝐴,𝐵𝐵,𝑞𝑞 =𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝐶𝐶𝑖𝑖𝐶𝐶𝐶𝐶 𝐿𝐿𝐶𝐶𝐿𝐿𝐶𝐶𝐿𝐿 𝑖𝑖𝐶𝐶 𝐶𝐶ℎ𝐶𝐶 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝐼𝐼𝐶𝐶𝑖𝑖𝐶𝐶𝑖𝑖𝐼𝐼𝐶𝐶𝑖𝑖𝐶𝐶𝐶𝐶 𝑀𝑀𝐼𝐼𝐷𝐷𝑀𝑀𝐶𝐶𝐶𝐶𝐵𝐵,𝑞𝑞

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝐶𝐶𝑖𝑖𝐶𝐶𝐶𝐶 𝐿𝐿𝐶𝐶𝐿𝐿𝐶𝐶𝐿𝐿 𝑖𝑖𝐶𝐶 𝐶𝐶ℎ𝐶𝐶 𝑆𝑆ℎ𝐶𝐶𝑜𝑜𝑀𝑀𝐶𝐶𝑜𝑜 𝑀𝑀𝐼𝐼𝐷𝐷𝑀𝑀𝐶𝐶𝐶𝐶𝐴𝐴,𝑞𝑞 (2)

where subscripts i, A, B, and q refer to firm i, shocked market A, initiation market B, and quarter q,

respectively. The main independent variable, Shocked Follower, is a dummy that equals one if the rival

was a follower in the shocked market, and zero if the rival was a leader. Note that we do not include a

variable for follower status of the firm in the initiation market, since over 92% of initiations occur in

markets that are new to the firm. In addition, we do not include the usual competition variable (for either

market) as an independent regressor because these are used to calculate the dependent variable. Instead,

we proxy the level of competition using the number of approved products in a market. Although the

62 We illustrate this with the following example: Firm X initiates a drug project in 2015q1 in market C. In 2014q1, firm X was shocked in market A, and in 2014q4, firm X was shocked in market B. Accordingly, firm X will have two observations in the sample. The shocked market in the first (second) observation is market A (B), and the initiation market in both observations is market C. We include the market (e.g., competition level) and firm-market (e.g., follower or leader) characteristics for both the shocked market and the initiation market in both observations. 63 Note that about 60% of the sample have a drug initiation matched to 3 or less shocked markets and about 10% of the sample have a drug initiation matched to 9 or more shocked markets.

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number of approved products within a market does not completely capture the level of development

activity within a market, it does capture one version of competition faced – the actual number of products

a firm must compete with once its product is approved.

Panel B of Table 6 provides summary statistics for this sample. On average, rivals initiate drug

projects in markets that are less competitive (96% as competitive) than shocked markets. Also on average,

about 72% of firms in shocked markets are followers. Drug Initiation markets appear smaller than shocked

markets; average number of approved products in initiation markets is 8.2, while average number of

approved products in shocked markets is 11.5. In addition, the growth in approved products is lower in

the initiation markets relative to the shocked markets. Finally, rivals have more projects in the shocked

market relative to the initiation market, which is expected since drug initiations are often the first time a

firm operates in a new market.

The specific regression is presented in equation (3).

𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝐴𝐴,𝐵𝐵,𝑞𝑞 = 𝐵𝐵1𝑆𝑆ℎ𝑜𝑜𝑜𝑜𝑀𝑀𝐼𝐼𝑜𝑜 𝐹𝐹𝑜𝑜𝑇𝑇𝑇𝑇𝑜𝑜𝐺𝐺𝐼𝐼𝑀𝑀𝑖𝑖,𝐴𝐴,𝑞�