investigating r&d productivity in the biotechnology and...
TRANSCRIPT
Investigating R&D Productivity in the
Biotechnology and Pharmaceutical
Sector and its Relationship with M&A
Activity
Undergraduate Senior Independent Work
in Candidacy for the Degree
of Bachelor of Arts
Princeton University
The Department of Economics
Adviser: Professor Swati Bhatt
April 2016
c© Copyright by --------, 2016. All
rights reserved.
Abstract
With declines in R&D productivity and the inability to innovate as efficiently as be-
fore, biopharmaceutical firms have begun shifting away from internal R&D expansion
to strategic alternatives such as M&A. Given the record breaking levels of M&A in
the biopharma sector over the past decade, this study aims to investigate the relation-
ship between a firm’s NME approval defined and shareholder returns defined R&D
productivity, and the number and volume of acquisitions the firm undertakes. Using
several multi-factor models with 0-3 years of lag, this study corroborates anecdotal
evidence and supports our hypothesis that lower levels of R&D productivity is signif-
icantly associated with later levels of M&A activity, though M&A activity in general
does not impact the R&D productivity of the acquirer. A further breakdown of ac-
quirer size reveals that large cap firms experience decreases to R&D productivity pro
forma compared to small caps which generally realize slight positive, yet insignificant
increases to R&D productivity.
iii
Acknowledgements
I would like to thank the Princeton Economics Department for their funding of my
participation in the 2015 FMA conference, which helped form many initial ideas for
this thesis, as well as making available the necessary statistical analysis software
including Compustat, SDC, Bloomberg, and Stata. I would also like to thank Credit
Suisse’s Healthcare IB team for their introduction to various healthcare services and
biotech companies which inspired the writing of this paper.
I would like to thank my many inspirational friends who have put a smile on my
face every day for these past four years. Thank you Mom, Dad, and Gina for your
unconditional love, support, and encouragement throughout my entire life.
Finally, I would like to thank my senior thesis adviser, Professor Swati Bhatt.
Thank you for not only your many edits and insights on the topic, but perhaps
more importantly, for serving as a role model and guiding me since Day 1. As my
freshman year academic adviser, sophomore year professor, then junior and senior
year independent work adviser, you have shaped my Princeton career in the most
positive ways possible.
iv
Contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
1 Introduction 1
2 Literature Review 11
2.1 Decline of R&D Productivity . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Determinants of M&A . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.1 Defensive Motives . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.2 Offensive Motives . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Effects of Mergers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3 Methodology and Data 21
3.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.1 R&D Productivity . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.2 Fundamental Financial Parameter Selection . . . . . . . . . . 23
3.1.3 R&D Productivity on M&A Activity . . . . . . . . . . . . . . 24
3.1.4 Regression Summary and Robustness . . . . . . . . . . . . . . 25
3.1.5 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
v
3.2.1 Fundamental Financial Data . . . . . . . . . . . . . . . . . . . 27
3.2.2 NME FDA Approval Data . . . . . . . . . . . . . . . . . . . . 32
3.2.3 M&A Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.4 Summary Statistics and Correlation Matrix . . . . . . . . . . 35
4 Results 36
4.1 Effects of M&A on R&D Productivity . . . . . . . . . . . . . . . . . . 36
4.1.1 General Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.1.2 Analysis by Size . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2 Effects of R&D Productivity on M&A . . . . . . . . . . . . . . . . . . 47
4.3 Diagram Summaries of Results . . . . . . . . . . . . . . . . . . . . . . 57
5 Discussion 59
5.1 M&A and R&D Productivity Relationship . . . . . . . . . . . . . . . 59
5.2 Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.3 Limitations and Future Work . . . . . . . . . . . . . . . . . . . . . . 65
6 Conclusion 67
Pledge 69
References 70
vi
List of Tables
1.1 Largest Life Sciences Deals Announced from 2013-2016 YTD . . . . 6
3.1 Removed Data Points . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2 Summary Statistics for Relevant Variables . . . . . . . . . . . . . . . 35
3.3 Correlation Matrix for Fundamental Parameters . . . . . . . . . . . . 35
4.1 Effect of Acq Vol on NME R&D Prod. . . . . . . . . . . . . . . . . . 41
4.2 Effect of Acq Num on NME R&D Prod. . . . . . . . . . . . . . . . . 42
4.3 Effect of Acq Vol on Return R&D Prod. . . . . . . . . . . . . . . . . 43
4.4 Effect of Acq Num on Return R&D Prod. . . . . . . . . . . . . . . . 44
4.5 Effect of Acq Vol on NME R&D Prod. by Size . . . . . . . . . . . . . 47
4.6 Effect of Acq Num on NME R&D Prod. by Size . . . . . . . . . . . . 48
4.7 Effect of NME R&D Prod. on Acq Num . . . . . . . . . . . . . . . . 53
4.8 Effect of NME R&D Prod. on Acq Vol . . . . . . . . . . . . . . . . . 54
4.9 Effect of Return R&D Prod. on Acq Num . . . . . . . . . . . . . . . 55
4.10 Effect of Return R&D Prod. on Acq Vol . . . . . . . . . . . . . . . . 56
vii
List of Figures
1.1 Economic Return on R&D Investment for Top 10 Biopharma Players.
Source: McKinsey (2011) . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Number of FDA Drug Approvals from 1996-2014. Source: FDA Center
for CDER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 M&A Trend in the Biotechnology and Pharma Sectors. Source:
Bloomberg. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Largest 25 Acquisitions in the Biopharma Space . . . . . . . . . . . . 9
3.1 Shift in Global Demographics for the Biotech/Pharma Industry . . . 29
3.2 Returns for Biotech/Pharma Companies from 2000-2014 . . . . . . . 30
3.3 Fundamental Data for Pharma/Biotechs from 2000-2014 . . . . . . . 31
3.4 NME FDA Approvals from 2000-2014 . . . . . . . . . . . . . . . . . . 31
3.5 R&D Productivity (NME) from 2000-2014 . . . . . . . . . . . . . . . 33
3.6 NME Profitability from 2000-2014 . . . . . . . . . . . . . . . . . . . . 33
3.7 M&A Acquisition Volume in the Biopharma Industry from 2000-2014 34
4.1 R&D Productivity (NME) vs. Total Number of M&A Acquisitions . 38
4.2 R&D Productivity (NME) vs. Total M&A Volume . . . . . . . . . . 38
4.3 R&D Productivity (Returns) vs. Total Number of M&A Acquisitions 40
4.4 R&D Productivity (Returns) vs. Total M&A Volume . . . . . . . . . 40
4.5 R&D Productivity (NME) vs. Number of M&A Acquisitions by Size 45
viii
4.6 R&D Productivity (NME) vs. M&A Volume by Size . . . . . . . . . 46
4.7 Number of Acquisitions Across Time by R&D Productivity Level . . 50
4.8 Acquisition Volume Across Time by R&D Productivity Level . . . . . 50
4.9 General M&A Results . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.10 M&A Results by Size . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.11 General R&D Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.12 Combined R&D Productivity and M&A Results . . . . . . . . . . . . 58
ix
Chapter 1
Introduction
The world has entered a stage in which continuing medical advances have pro-
longed life expectancy and increased our dependence on methods for controlling
non-communicable diseases. With constant innovation, the biotechnology industry
has seen record levels of activity. In 2014, the biotechnology industry's market
capitalization crossed the USD $1 trillion threshold for the first time. Research
and development (R&D) spending surged 20%, a record $54.3 billion in capital was
raised, and 94 new US and European companies went public (Ernst & Young, 2015).
Underlying the strong health care markets are several dramatic industry-wide
changes that may have large implications on the growth of the industry in the near
future. One such trend beginning in the 1990s is the diminishing R&D productivity,
or the economic returns and innovation output produced by a given level of R&D
spend, experienced by large pharmaceutical and biotechnology firms that have begun
to find it increasingly difficult and costly to produce in-house drugs. According to
the Tufts Center for the Study of Drug Development (CSDD), higher costs are due to
the increased complexity of clinical trials, a greater focus on chronic and degenerative
diseases, and tests for insurers seeking comparative drug effectiveness data. At a
more basic level, the drug production process faces diminishing marginal returns
1
across time as the most cost- and time-efficient investments have already been made.
This trend was concisely summarized by Scannell et al. (2012) as the “better than
Beatles’ problem”:
“Imagine how hard it would be to achieve commercial success with new
pop songs if any new song had to be better than the Beatles, if the entire
Beatles catalogue was available for free, and if people did not get bored
with old Beatles records. We suggest something similar applies to the
discovery and development of new drugs. Yesterday’s blockbuster is to-
day’s generic. An ever-improving back catalogue of approved medicines
increases the complexity of the development process for new drugs, and
raises the evidential hurdles for approval, adoption and reimbursement.
It deters R&D in some areas, crowds R&D activity into hard-to-treat dis-
eases and reduces the economic value of as-yet-undiscovered drugs. The
problem is progressive and intractable.”
In addition to the “better than Beatles’ problem,” Scannell identifies three other
possible explanations for declining R&D productivity: the “cautious regulator prob-
lem,” which refers to the post-Vioxx increases in safety requirements for new drugs;
the “throw money at it” tendency, which is the tendency to keep pouring money and
resources into projects that may never succeed; and the “basic research-brute force
bias,” which is the industry's tendency to overestimate the probability that advances
in basic research will show a molecule safe and effective in clinical trials. Together,
these four factors have explained the decrease in R&D productivity over time.
Several empirical studies have been done to analyze this trend. Quantitatively,
a McKinsey (2011) study reveals that in the past 25 years, the industry has created
more than $1 trillion of shareholder value, but destroyed around $550 billion of value,
primarily via poor strategic decisions, from 2000 to 2010. During the same period,
R&D spending rates increased by over 60% from 10 to 16 percent of total sales.
2
Absolute spend also increased as worldwide sales grew from $200 billion in 1995 to
$800 billion in 2009. It is calculated that the average economic return on R&D for
the largest biopharma players has declined from between 13 and 15 percent in the
1990s to between 4 and 9 percent in the past decade (Figure 1.1). This suggests a
decline in R&D productivity across the biopharma space and that current organic
investment in R&D programs is not creating much value.
Figure 1.1: Economic Return on R&D Investment for Top 10 Biopharma Players.Source: McKinsey
In another study conducted by the CSDD, it was estimated that on average, costs
approximated $802 million in 2003 (a little over $1 billion today) to develop and
achieve marketing approval for a single drug. Follow-up studies show that the average
cost to develop a new drug has risen to over $2.5 billion in 2014. There has been clear
increases in R&D expense per year even though the number of FDA approvals for
3
new molecular entities (NMEs) and biological license applications (BLAs) each year
has remained fairly stable (DiMasi, 2003; DiMasi, 2014), perhaps due to the rise of
small firms focused on developing orphan drugs designed to treat specialty diseases
(Karst, 2015). Figure 1.2 shows the number of FDA-approved NMEs and BLAs from
1996-2014.
Figure 1.2: Number of FDA Drug Approvals from 1996-2014. Source: FDA Centerfor CDER
Declining R&D productivity has resulted in stakeholders and shareholders exert-
ing mounting pressure on boards, CEOs, and executive teams to acknowledge the
situation and reduce R&D costs. It has been proposed that firms begin to seek inor-
ganic means of growth and acquire many early phase drugs as a strategic alternative.
Such means include licensing, program partnerships, or company acquisitions (David,
2010).
4
Excluding the tech boom from 1999-2000, M&A volume in the biotech space has
increased substantially since the 1980s which may provide evidence of shifting trends
due to R&D productivity in the sector. Figure 1.3 highlights the M&A trend.
Figure 1.3: M&A Trend in the Biotechnology and Pharma Sectors. Source:Bloomberg.
In the past three years, many of the largest mergers and acquisitions have occurred
in the life sciences space. In 2013, three of the ten largest deals were within the bio-
pharma industry. 2014 saw similar trends with two of the ten largest deals happening
within the space, and 2015 also saw three of the ten largest deals, including the largest
deal in the space of all time: Allergan’s acquisition of Pfizer for $160 billion USD.
As of April 2, 2016, the largest deal announced YTD is within the biotech/pharma
space. Table 1.1 lists several of the largest deals within the past three years in the
space chronologically while Figure 1.4 presents the 25 largest deals that have ever
occurred in this space in bubble scatter chart format (StreetInsider, 2016).
5
Table 1.1: Largest Life Sciences Deals Announced from 2013-2016 YTD
Date Acquiror Target Value ($ in bn)01/11/2016 Shire Baxalta $32
11/23/2015 Pfizer Allergan $160
07/18/2014 Abbvie Shire $54*
05/26/2015 Abbvie Pharmacyclics $21
03/17/2015 Actavis Allergan $70.5
02/18/2014 Actavis Forest Laboratories $28
08/25/2013 Amgen Onyx Pharmaceuticals $10
05/28/2013 Valeant Bausch & Lomb $8.7
04/15/2013 Thermo Fisher Life Technologies $13.6
* Deal did not close
Theoretically, big pharma engage in M&A to acquire potentially commercially
viable products that can shave years off of R&D effort in a merger, whereas biotech
firms are often eager to gain regulatory and marketing expertise—the strengths of
big pharma. In addition, mergers among biotech firms have long been a way for
smaller companies to acquire complementary technologies and strengthen their finan-
cial footing (Rafferty, 2007; Allergen, 2015). Yet despite the theory and the flowery
management optimism surrounding these deals, the impacts of M&A are not certain.
Little empirical research exists that show the effects of M&A on R&D productivity in
the biotech space. Though Upadhyay (2014) attempts to do this, there were several
faults in the methodology including R&D productivity being weakly defined as only
number of FDA approvals and the use of M&A transactions as an absolute quantity
as opposed to a total value. The regressions have also been criticized for not holding
up to robustness checks. Despite the lack of academic literature on this relationship,
6
several recent examples reveal that M&A effects are not clear cut. This question is
addressed in further detail in Chapter 2.
In addition to looking at the effect of M&A activity on R&D productivity, it
would also be interesting to examine the reverse relationship: the effect of declining
R&D productivity on M&A activity. Though it is easy to see the societal and financial
magnitude of the life sciences industry, understanding the rationale behind biopharma
M&A decisions is not as clear. Among many reasons, one often cited cause as of late
is tax savings from an inversion. Typically in a corporate inversion, a U.S. domiciled
corporation forms a new subsidiary in a tax haven country via a merger, and the
haven-domiciled entity becomes the parent company of the firm's U.S. and foreign
operations. As implied, these inversions take advantage of the relatively lower tax
rates enjoyed in the tax haven country and reduce the financial statement effective
tax rate (ETR) to improve both earnings and cash flows, regardless of any other
synergies that can be realized between the two companies, including improvements
to R&D productivity. The U.S. Treasury Department has cited tax savings from
the avoidance of U.S. tax on U.S.-source earnings as a primary reason for corporate
inversion deals (2002).
From 2013 to 2014, the health care sector saw a slew of tax inversion deals. Sev-
eral examples include Pfizer's unsuccessful attempt to acquire UK-based AstraZeneca
and AbbVie's agreement to acquire UK-based Shire, shown earlier in Table 1.1. To
quantify the inversion strategy, AbbVie stated in a June 25, 2014 statement that,
ignoring any realizable complementing portfolio synergies, the company would cut its
overall effective tax rate from 22.6% in 2013 to 13% by 2016. Even if AbbVie did not
redomicile, it would yield over $500 million in tax savings as the company generates
minimal cash flow in the United States (AbbVie, 2014). Like many pharmaceutical
companies, AbbVie holds its cash in overseas subsidiaries and pays negligible taxes on
it. Other such moves have been made by companies like Mylan, Salix, Actavis, Per-
7
rigo, Auxilium, and Jazz. Even with reformed regulation that severely disincentives
otherwise synergy-less inversion deals, companies are still finding ways to achieve tax
savings via inversion.
For example, in the Pfizer-Allergan deal, Pfizer is expected to cut its tax rate from
25% in 2014 to 17% in the first year of the deal. Due to its size, the inversion deal
evades restrictions imposed in November 2015 that prohibited inversion where the US
company ends up with a combined stake between 60-80%. The Pfizer-Allergan deal
results in Pfizer shareholders owning 56% of the new company (Allergan, 2015).
Another commonly cited catalyst for recent M&A activity is the Patient Protec-
tion and Affordable Care Act, signed in 2010. Among the many consequences of the
sweeping changes to the health care industry, margins are likely to decrease for drug
producing biotech companies. With the previous fragmented health care system that
had no drug pricing authority, biotechs often enjoyed large margins at the expense of
many corporations that were responsible for about two thirds of all Americans who
are covered by health insurance through their employer. As a result, Americans spent
more on prescription drugs than citizens in any other country (Lehmann, 2011). With
a more structured health care system that creates increased Medicare and Medicaid
plans, the Affordable Care Act will cut into the profits of biotechs which pay signif-
icant rebates to the states and federal government for every treatment participants
in these government programs receive. Anecdotal evidence has cited ACA-induced
lower profitability and declining margins in general to be a large stimulus for merger
activity (Bourne Partners, 2015).
8
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9
Though this study does not comprehensively examine all potential causes of M&A,
examining acquisitions in the context of the recent declines in returns to R&D would
be useful from both a financial perspective in the forming of future expectations in
the life sciences industry and from a social and perhaps ethical perspective when
considering the economic incentives leading to the production of drugs for certain
diseases over others. This study aims to investigate the direction and strength of the
relationship between R&D productivity and M&A activity and whether/how certain
factors affect this relationship.
The remainder of the paper is organized as follows. Chapter 2 provides a literature
background of the academic debate surrounding R&D productivity and merger theory
and effects. Chapter 3 provides details around the methodology of the data analysis,
including sources from where the data are derived and the multi-factor model utilized
in the study. Chapter 4 validates existing empirical research and tests the significance
of the R&D productivity and M&A parameters in various conditions. Chapter 5
provides explanations and implications of the findings in both an academic and real-
world context. Chapter 6 concludes.
10
Chapter 2
Literature Review
2.1 Decline of R&D Productivity
Advances in medical technologies and in the understanding of infections and diseases
at the molecular level have drastically increased the base of possible biological targets
for the development of innovative medications in past few decades. However, while
investment in pharmaceutical R&D has increased substantially during this time, a
lack of increase in FDA approved output indicates an industry-wide diminishing of
therapeutic innovation and R&D productivity. Though broadly examined in Chapter
1, a difficulty in this study is precisely quantifying R&D productivity to measure it
across time. Data for many variables that may be considered under the umbrella
of R&D productivity, including work in process, the probability of technical success
(PoS), and the overall value of the drug by the cost and the cycle time, are infeasible
to procure or simply do not exist on a large scale (Paul 2010). Pammolli (2011)
simplifies R&D productivity by comparing outputs to inputs, namely taking FDA-
approved New Molecular Entities (NMEs) created as a function of the PoS of clinical
trials and changes in the number of new projects started each period.
11
A New Molecular Entity, as defined by the FDA, is a drug that contains an active
moiety that has never been approved by the FDA or marketed in the US. There
are specific requirements for clinical trials in terms of efficacy and safety outlined by
the FDA. NMEs are the main source of value for the pharmaceutical and biotech
industry, and arguably society at large, as they allow for the commercialization of
highly profitable and effective drugs. Though not inclusive of all returns of pharma
and biotech companies, NMEs are an ultimate end goal for most companies and
thus provide a good measure of R&D outputs (Pammolli 2011). As an extension of
Pammolli’s work, Scannell (2012) analyzes pharmaceutical R&D productivity as the
number of FDA approvals per billion USD in R&D spend. This ratio makes sense
and is a starting point for the purposes of this study. Limitations for this method are
discussed in Chapters 3 and 5. Confirming empirical literature, Scannell found that
this ratio has been declining for the past twenty years, a trend he terms “Eroom’s
Law.”
Some form of Eroom’s Law already appears to be affecting financial markets,
and the impact is being seen in vast cost-cutting measures implemented by major
pharmaceuticals. The stock prices of pharmaceuticals indicate that investors expect
the financial returns on current and future R&D investments to be below the cost of
capital at an industry level (Tollman et al., 2011), and would prefer higher dividends
to higher levels of R&D spend. Even if investors may be incorrect in this sentiment,
as they have little reason to be optimistically biased towards Eroom’s Law being
successfully counteracted when there exists safer investment options, their perceptions
are not trivial as they ultimately control the company via executive appointments and
resource allocation control. Experts believe that Pfizer, Merck Co., AstraZeneca,
and Eli Lilly will be spending less, in nominal terms, in 2016 than they did in 2010,
partly in response to shareholder pressure (Scannell, 2012). Across the top ten large
pharmaceutical companies, nominal R&D spending has been flat for the past several
12
years, which represents a decline in real terms. More importantly, the combined effect
of declining real-term R&D spending with Eroom’s Law (fewer new drugs per billion
US dollars of R&D investment over time) is that there will be fewer new drugs and/or
drugs will become inordinately expensive. The latter has already made large headlines
this year with the infamous cases of Martin Shkreli and Valeant Pharmaceuticals.
Ultimately, this trend has a large potential to threaten the real benefits (Ford et al.,
2000; Lichtenberg, 2005) that follow from the availability of effective and affordable
new drugs.
Further compounded by the increasing costs of more stringent regulation in the
drug markets, the declining productivity in the industry aligns with the increase in
M&A transactions within the recent years. M&A is an oft invoked strategy to appease
shareholders in an attempt to improve R&D productivity.
2.2 Determinants of M&A
The motives for most M&A transactions can be generally categorized into defensive
or offensive rationales (Burns et al., 2005). This section is divided into these two
strategies to identify and explain the key drivers of M&A activity in the biopharma
sector. As this study is focused on the relationship between mergers and R&D pro-
ductivity, it is important to understand the rationales for mergers as well as evaluate
studies that focus on their effects. While alliances and partnerships that lead to
the co-development of drugs are a large part of the biopharma industry, for practi-
cal purposes they are not considered in this large scale study spanning thousands of
firms.
13
2.2.1 Defensive Motives
The earliest and most relevant hypothesis is that industry-wide shocks can precip-
itate merger waves, a thesis that originates back to Gort (1969). Research shows
that industry-wide shocks appear to explain merger waves in other industries such as
banking and telecommunications in the 1990s (Andrade et al., 2001). Furthermore,
the bursting of the dot com and housing bubbles led to another wave of mergers
amongst tech and financial companies in 2002 and 2008 respectively (Aharon et al.,
2010). In the context of biopharma, the economic environment faced greater chal-
lenges and pipeline gaps became increasing prevalent throughout the industry by the
1990s. With stock prices under pressure, many affected drug firms began directing
free cash flows more towards the acquisition of other firms’ products and pipeline and
less towards in-house drug production. Bidders could often pay the premium associ-
ated with these acquisitions with pro-forma vertical integration synergies and SG&A
savings (Grabowski & Kyle, 2008). Various researchers also asserted that mergers
and acquisitions facilitate necessary, yet disruptive organizational change that would
otherwise be met with substantial internal resistance and gridlock (Ravenscraft and
Long, 2000). On the other hand, mergers are also associated with substantial inte-
gration costs and cultural conflicts that can hinder the productivity of the firm in the
post-merger period (Clark, 2001; Larsson and Finkelstein, 1999).
Ravenscraft and Long (2000) performed one of the first analyses of pharmaceutical
mergers. Their analysis covered mergers of significant value from between 1985 and
1996. Using event studies, they found that large horizontal mergers in general created
value in the overall stock market. As found in other industry studies, however, most
of these returns are captured by target firms. Their findings are consistent with
the response to the aforementioned industry shocks, excessive capacity hypothesis.
In the context of R&D, cost cutting for large horizontal biopharma mergers was
found to reduce employee headcount in R&D divisions anywhere from 8 to 20 percent
14
of the combined R&D workforce. Furthermore, there was a consolidation of R&D
laboratories and elimination of marginal R&D projects by several firms.
Another study by CenterWatch of 11 large mergers in the biopharma sector that
occurred from 1989 to 1998 reported a 34 percent average reduction in development
projects three years post-merger (CenterWatch, 2000). While these two studies im-
ply a somewhat negative effect on R&D spend via M&A, neither the CenterWatch
study nor Ravenscraft and Long’s analysis examined subsequent effects on the firms’
FDA approval-based R&D productivity. Furthermore, findings are not clear cut with
regards to R&D productivity. While cutting R&D labs may result in a longer time
to realize FDA approvals, lower costs to do so via the elimination of duplicate efforts
or projects with low probability of success may improve the companies’ pro-forma
R&D performance. It is also important to note that these studies were conducted
over 15 years ago with data derived from only a small set of companies from the
20th century. Given the industry-wide changes and the further diminishing returns
of R&D, a current, NME approval defined R&D productivity analysis is warranted.
Two other analyses have found that pipeline gaps and issues continue to be a
key driver of merger activity. A study of 202 biotechnology and pharmaceutical
mergers from 1998 to 2001 found that biopharma firms with relatively old portfolios
of marketed drugs exhibit a higher propensity to acquire another firm (Danzon et
al., 2007). A second study of 160 pharmaceutical mergers between 1994 and 2001
found that firms with weakness in their R&D pipeline and fewer years of exclusivity
on their marketed drugs had a greater probability of engaging in M&A (Higgins and
Rodriguez, 2006). Though these studies serve as a useful guideline and help shape
expectations for this study, both studies are once again relatively outdated, focus on
non-approval based means of measuring R&D productivity, and are limited in the
number of firms in their sample set.
15
2.2.2 Offensive Motives
Not including glory-seeking reasons, proactive motives for mergers include increases
in size and information to achieve cost advantages associated with economies of scale
in R&D, among other firm programs. Firms may also merge to increase the breadth
of therapeutic areas in their respective R&D divisions. Furthermore, pharmaceutical
firms engage in M&A activity to incorporate new technological and research related
efficiencies into the firm to enhance productivity. Mergers may also simply be a
management tactic to reach pre-set firm size and growth rate goals, even if they would
not result in tangible increases in profitability and productivity (Mueller, 1986).
Given that many recent headlines have surrounded the activities of large bio-
pharma firms, and that mergers have an explicit purpose of increasing firm size to
realize economies of scale, it would be interesting to examine R&D productivity and
mergers in the context of size. Cockburn and Henderson published a series of papers
(Cockburn & Henderson, 2001; Henderson & Cockburn, 1996) focusing on economies
of scale in drug R&D. Their studies analyzed the effects of scale on productivity at
the research program level, for ten large pharmaceuticals. Despite a small sample
set, these studies may have broader implications as they utilize highly detailed data,
including program-level R&D spending and drug output, over a long time horizon.
They found that large firms engaged in a broader scope of research activities yielded
higher drug output than focused firms, but that scale did not matter much once
scope is controlled for. In other words, economies of scope were positively associated
with higher productivity while economies of scale were not. A paper by Danzon et
al. (2005) finds a slight, positive association between productivity and a company’s
development experience as measured by the number of different types of drugs in
clinical trials, but these benefits are also subject to diminishing returns.
The consolidation of the larger pharmaceutical firms, such as those shown in Figure
1.4, has often resulted in increasing breadth in the R&D activities across therapeutic
16
categories. For example, Pfizer, as a result of its merger with Pharmacia in 2002,
improved its presence in immunology by adding Pharmacia’s Celebrex and Bextra
legacy drugs into its pipeline. Furthermore, Pfizer developed R&D programs in new
fields such as oncology, endocrinology, and ophthalmology which complemented the
company’s historical strengths in erectile dysfunction, cardiovascular disease, depres-
sion, and central nervous system (Burns et al., 2005). This increased breadth may
produce economies of scope benefits over time as described in the academic literature
by Cockburn and Henderson. However, Pfizer, among the other leading pharmaceu-
tical firms, now manages an annual R&D budget of well over $7 billion. At this size,
companies have entered a level of diminishing returns from the standpoint of man-
aging, motivating, and coordinating the activities of employees. It is notable that
many pharmaceuticals with multi-billion dollar R&D budgets and hundreds of R&D
projects are instituting more flexible organizational structures and delegating more
decision-making power to the heads of the various therapeutic area divisions in an
attempt to improve productivity (Grabowski, 2008).
Beyond economies of scale, life sciences companies may pursue M&A to become
competitive in an emerging, high-growth therapeutic area. M&A would provide a
quicker and perhaps cheaper means of entering high opportunity fields compared to
internal expansion. It can take many years or even decades to build, in-house, the
necessary scientific capabilities to enter a new therapeutic category or implement
a new research platform in an emerging field. This appears to be an important
motivation underlying M&A activity by established pharmaceutical firms (Grabowski,
2008), though no studies have been done to analyze its effects on R&D productivity.
Another traditional economic motive for mergers can be to increase market share
and market power to become more competitively positioned. This has not been the
norm for large biopharma mergers. Margins in the biotech industry are determined
more by innovation of products than strict price cutting, thus traditional increases
17
in market share do not share the same advantages in the biopharma space as it
would in other sectors. Furthermore, in the US and Europe, mergers are subject to
scrutiny prior to their implementation by antitrust authorities (Mueller, 1996). The
Department of Justice clearly defines guidelines on what economic parameters can
trigger challenges to a proposed merger. In the case of pharmaceuticals, markets
are defined in terms of therapeutic categories, explaining recent cases such as the
Pfizer-Allergan merger which, despite being a $160 billion deal, has little overlap
between the companies’ respective therapeutic areas. Significant mergers of firms with
similar pipelines, therefore, must go through serious reviews before implementation.
These negotiations can result in the spinning off of competitive products in the same
therapeutic areas as a condition of carrying out the merger. Anti-competitive issues
and potential competition effects concern more the number of late stage competitors
for legacy product candidates than the absolute size in terms of sales or market cap
of the combined entity.
2.3 Effects of Mergers
Much research focuses on the impacts of M&A on shareholder return over specific
event horizons for firms across all sectors. Among those studies, most have been
dedicated to understanding how and for whom wealth is created for through M&A.
Many theories have emerged, for example, the economies of scale theory (Houston
et al., 2001; Ravenscraft, 1989), synergies theory (Bradley et al., 1988), monopoly
theory of mergers (Eckbo, 1992; Ravenscraft & Scherer, 1987; Mueller, 1985), market
power theory (Anand & Singh, 1997; Baker & Bresnehan, 1985; Barton & Sherman,
1984), diversification theory (Berger & Ofek, 1995), and redeployment of assets theory
(Capron, 1999). In general, the bulk of the research focusing on the wealth effect of
M&A appears to imply that the return to acquiring firm shareholders, on average,
18
is close to zero (Andrade et al., 2001, Kohlers & Kohlers, 2000; Eckbo & Thorburn,
2000; Lyroudi et al., 1999; Schwert, 1996) and that the majority of wealth goes to the
shareholders of the target firm (Bruner, 2002; Houston et al., 2001; DeLong, 2001;
Eckbo & Thorburn, 2000; Jarrell et al., 1988; Jensen & Ruback, 1983). Few studies
are able to demonstrate meaningful wealth effects for the acquirer in non-tender offer
M&A.
However, in sharp contrast with the extensive literature that exists on the impact
of M&As on the financial and economic performance of companies, only a limited
number of studies, many of which are outdated, focus directly on the consequences
of M&As on the companies’ innovation output. Though briefly alluded to in several
studies provided in the previous two sections, the results of these studies are once
again mixed in nature and raise a number of issues and questions for further research.
Andrade (2004) finds that M&A activity in the biopharma sector generally has a
more focused range of goals by nature. Consolidation not only allows for a larger cap-
ital base for development, but also facilitates the exchange of patented information
and technology among the merged entities with the improved probability of further
producing positive NPV drugs at lower expenses. The likelihood of pursuing an M&A
transaction is positively associated with the current status of a firm’s internal produc-
tivity, or, as Higgins, et al. refers to, its desperation index. The level of desperation
is determined by the change in the weighted value of a company’s pipeline products
immediately prior to the transaction occurring. If the company is experiencing in-
creased desperation in the form of deteriorating pipeline quality and sales, perhaps
due to patent expirations and low levels of innovation, they have a higher likelihood
of pursuing M&A alternatives (2006).
Generally, studies analyzing merger effects of pharmaceutical companies find a
slight negative effect on R&D performance. This may be because there is no additional
advantage to size at the level for most mergers, and/or because mergers are a response
19
to distress, in which case the counterfactual is hard to determine (Grabowski &
Kyle, 2008). However, depending on the situation, R&D inputs may increase as a
consequence of M&A. It has been argued that the lower investment may be due to
the removal of duplicate expenditures or through realizing other cost-related synergies
(Hitt, 1991; Ornaghi, 2009). In certain cases, R&D investments can increase if the
M&A transaction allows the acquiring firm to either eliminate common R&D inputs
or achieve other synergies such as combining different R&D and knowledge inputs.
Thus, the effects of M&A on R&D productivity and R&D investments in general are
not clear cut (Cassiman, 2005).
In summary, while plenty of research exists on the effects and causes of mergers,
there is little current academic research regarding the interaction between M&A and
R&D productivity within the biopharma sector. The existing literature utilizes data
before the 21st century, only measures positive association between variables that
do not fully capture the idea of R&D productivity, and/or utilizes small sample sets
(Ahuja & Katila, 2001; Ernst & Vitt, 2000; Bresman et al., 1999; Capron, 1999;
Capron et al., 1998; Chakrabarti et al., 1994; Grandstand & SjOlander, 1990). It
would be interesting to reexamine the question from a more modern perspective
and with different econometric models involving an R&D productivity variable that
encompasses post-merger FDA approvals to investigate the relationship between R&D
productivity and M&A activity. Furthermore, with widespread industry changes, it
would be interesting to investigate the strength of declining R&D productivity as a
variable in explaining M&A activity, if such a relationship exists.
20
Chapter 3
Methodology and Data
3.1 Methodology
3.1.1 R&D Productivity
A valid framework is necessary to test and measure the R&D productivity for phar-
maceutical and biotech companies. For the purposes of this study, a basic model
suggested by Pammolli, Magazzini, & Riccaboni (2011) appears to be an appropriate
starting point. This model uses NME approvals as a proxy for productivity, which
although does not reflect changes in the quality of the output, suffices for simple
analysis of R&D productivity. Pammolli uses a ratio of expected number of NMEs,
dependant on POS and total number of projects, across two time periods to calculate
changes in R&D productivity. Because such data is publicly unavailable, a ratio of
FDA NME approvals to R&D expenditure, as was utilized in Scannell (2012), will be
used for this study. With these limitations in mind, however, a model using share-
holder returns, which reflects the success or failure of every clinical trial and other
operations, is also used. These two basic R&D productivity models are defined in
Equations 3.1 and 3.2:
21
RDPy =NMEy
RDEy
(3.1)
RDPy =Ry
RDEy
(3.2)
RDPy is the R&D productivity in year y
NMEy is the number of FDA NME approvals in year y
RDEy is the R&D expenditure in year y
Ry is the shareholder return for the company in year y
To measure the impact of M&A activity on R&D productivity, we use both the
number of mergers and acquisitions a firm has undertaken in a specific year as well
as the volume (dollar amount of M&A transactions). In addition to controlling for
macro data, fixed effects dummies for year, firms’ country of domicile, and firms’ sub-
industry are incorporated to isolate the effect of M&A activity on R&D productivity.
RDPy = β1 ∗MAn,y + β2 ∗ FEy + α (3.3)
RDPy = β1 ∗MAv,y + β2 ∗ FEy + α (3.4)
RDPy is the R&D productivity, measured by both NMEs and returns, in year y
MAn,y is the number of M&A transactions for the company in year y
MAv,y is the volume of M&A transactions for the company in year y
FEy are the fixed effects for the company in year y
Finally, fundamental financials will need to be accounted for in the model to ensure
the improvements to productivity are coming from the M&A activity undertaken by
the firm and not other means which may be reflected in the financials, such as higher
levels of revenue. The chosen factors are explained in the following section.
22
3.1.2 Fundamental Financial Parameter Selection
As seen from criticisms of studies such as Gordon (1959), risk variation among firms
may lead to omitted bias in and inflation of the coefficients of independent variables
including M&A activity. Thus, to begin, we will use annual GDP to control for annual
macro data, especially given the turmoil of the 2008 financial crisis.
Furthermore, to deduce the significance of M&A activity itself and its implication
for management, fundamentals will need to be accounted for. Sales, net income, which
is a generally good parameter for retained earnings, market capitalization, and ROICs
will be controlled for. These factors are in-line with Hou, Xue, and Zhang's argument
in “Digesting Anomalies: An Investment Approach,” which looks through 80 factors
to find the most statistically significant parameters in determining returns. The paper
concludes, “. . . an empirical q-factor model consisting of the market factor, a size
factor, an investment factor, and a profitability factor outperforms the Fama-French
and Carhart models in capturing many (but not all) of the significant anomalies”
(2014). Furthermore, a market factor will be used when regressing on shareholder
return defined RDP as individual stock return will depend heavily on general market
behavior.
Finally, different lags will be applied to take into consideration that M&A activity
generally produces synergistic effects, such as possible improvements to R&D produc-
tivity, several years into the future. While complete synergies may be reflected years
or even decades into the future, most synergies are realized in the first several years.
Thus lags of zero, one, two, and three years will be applied to the M&A variables for
simplicity.
RDPNME,y = β1MAi,y−l + β2FEi,y + β3Si,y + β4NIi,y + β5ROIi,y
+ β6MCi,y + β7GDPy + α
(3.5)
23
RDPR,y = β1MAi,y−l + β2FEi,y + β3Si,y + β4NIi,y + β5ROIi,y
+ β6MCi,y + β7GDPy + β8Rm,y + α
(3.6)
MAi,y−l is the volume/number of acquisitions undertaken by company i in year y-l
Si,y is the annual fiscal year end revenue for company i in year y
NIi,y is the annual fiscal year end net income for company i in year y
ROIi,y is the return on invested capital for company i in year y
MCi,y is the equity market capitalization for company i in year y
GDPy is the annual US GDP in year y
Rm,y is the return of the market in year y
3.1.3 R&D Productivity on M&A Activity
The second aim of this study is to investigate the effects of declining R&D produc-
tivity on the likelihood of pursuing mergers and acquisitions. Since it is infeasible to
utilize a multivariate regression model to control for every individual factor affecting
decisions to undertake M&A activities, we separate the companies by level of R&D
productivity. Specifically, a dummy variable indicating whether or not a company’s
R&D productivity is among the top 50 percentile of the data set is created and used
as a dependent variable when regressing on M&A activity. The coefficient in front
of the dummy variable will indicate the degree to which R&D productivity affects
M&A activity. Such a technique controls for many otherwise uncontrollable factors,
especially the political legislation and atmosphere surrounding the healthcare indus-
try (incentives for inversion, Affordable Care Act, etc.). As in Part 1, sub-industry,
geography, and year fixed effects will be included in the regression model. The model
is as follows:
24
MAi,v,n,y = β1RDPi,y−l + β2HLy + β3FE + α (3.7)
RDPy−l is the NME and returns R&D productivity for company i in year y-l
MAi,y is the volume/number of acquisitions undertaken by company i in year y
HLy is a dummy variable indicating if company i’s R&D productivity is in the top
50 percentile of all dataset companies
Only a one year lagged variable is considered in this regression as where M&A
activity may take a few years before its effects on R&D productivity may be realized,
R&D productivity is generally fairly apparent and would be reflective in quarterly
earnings and strategic alternatives such as M&A would be utilized relatively quickly
to appease shareholders (Lipton, 2006).
3.1.4 Regression Summary and Robustness
In summary, the main regressions run to investigate the relationship between M&A
activity and R&D productivity are shown below with descriptions. Given existing
empirical literature and the nature of the question, OLS estimation seems to be
the best model. A White test fails to reject the null of homoskedasticity for every
regression at an α = 0.10 level. Furthermore, the absolute value of error terms do not
increase as the size of firm increases. Since we use multiple methodologies to define
R&D productivity and M&A activity, similar results across the specifications would
imply a certain level of robustness.
NMEi,y
RDEi,y
= β1MAi,n,y−l + β2FEi,y + (Fund.) + α (3.8)
25
NMEi,y
RDEi,y
= β1MAi,v,y−l + β2FEi,y + (Fund.) + α (3.9)
Ri,y
RDEi,y
= β1MAi,n,y−l + β2Rm,y + β3FEi,y + (Fund.) + α (3.10)
Ri,y
RDEi,y
= β1MAi,v,y−l + β2Rm,y + β3FEi,y + (Fund.) + α (3.11)
MAi,n,y = β1
NMEi,y−l
RDEi,y−l
+ β2HLy + β3FE + α (3.12)
MAi,n,y = β1
Ri,y−l
RDEi,y−l
+ β2HLy + β3FE + α (3.13)
MAi,v,y = β1
NMEi,y−l
RDEi,y−l
+ β2HLy + β3FE + α (3.14)
MAi,v,y = β1
Ri,y−l
RDEi,y−l
+ β2HLy + β3FE + α (3.15)
(3.8)Effects of total number of M&A transactions l years before year y for com-pany i on NME defined R&D productivity in year y
(3.9)Effects of total volume of M&A transactions l years before year y for com-pany i on NME defined R&D productivity in year y
(3.10)Effects of total number of M&A transactions l years before year y for com-pany i on shareholder return defined R&D productivity. Controls for marketreturns
26
(3.11)Effects of total volume of M&A transactions l years before year y for com-pany i on shareholder return defined R&D productivity. Controls for marketreturns
(3.12)Effects of NME defined R&D productivity on total number of M&A trans-actions l years before year y for company i
(3.13)Effects of shareholder return defined R&D productivity on total number ofM&A transactions l years before year y for company i
(3.14)Effects of NME defined R&D productivity on total volume of M&A trans-actions l years before year y for company i
(3.15)Effects of shareholder return defined R&D productivity on total volume ofM&A transactions l years before year y for company i
3.1.5 Hypothesis
Given recent anecdotal evidence, a comprehensive multiple factor analysis on various
specifications of R&D productivity and M&A may reveal significance in using the
declining R&D productivity to explain higher levels of M&A volume and number
of transactions. On the contrary, given the literature on merger effects in Chapter
2, it is quite possible that M&A would yield insignificant positive changes to R&D
productivity, especially amongst large firms.
3.2 Data
3.2.1 Fundamental Financial Data
Several sources of data were utilized for this study. Bloomberg was used to pull the
ISIN identifiers of all publicly traded companies in the Pharmaceuticals, Biotechnol-
ogy, and Life Sciences industry from 2000-2014. Using these identifiers, historical
corporate financial data were retrieved from the S&P Capital IQ's Compustat North
American database, which contains fundamental and market data on publicly held
companies. The database covers 99% of the world’s total market capitalization with
27
annual company data available back to 1950. In addition to total shareholder returns,
fundamental financial data obtained include ROIC, sales, market cap, net income, and
R&D expense. All financial data were converted into USD at the exchange rate at
the time of reporting. Market return and annual GDP data were obtained from the
Federal Reserve of St. Louis database.
The data was scrubbed to only include data points with values for the aforemen-
tioned financial variables. Observations with ROICs above 1000% and below -1000%,
negative R&D values, and market capitalization below $1 million USD were removed.
Additionally, several extreme outlier data points were removed as they were likely due
to reporting errors. The removed data points are shown in Table 3.1. Ultimately, the
data set comprised of 9,705 firm-year observations with 1,462 total number of unique
firms. The complete summary statistics and correlation matrix can be found at the
end of this section.
Table 3.1: Removed Data PointsName Year Sales R&D Spend Market Cap Return
Anxin-China Holdings 2003 26,213,376 -26,000 448,224,000 0.0%IBIO Inc. 2014 205,000 -150,000 44,964,835 105%China YCT Intl 2013 33,102,883 1,756,053 11,865,209 13,233%Synovics Pharm 2004 98,767 250 16,802,730 10,400%Ondine Biomedical 2004 0 3,504,530 95,639,924 6,400%RNL Bio 2005 5,013,627 2.260e+08 1.795e+08 4,199%
Data included international companies. Figure 3.1 shows that North America
has been the leader in the number of total domiciled companies, but its share has
declined over the years as increased incentives to invert have grown and developing
countries continue to improve their health care systems. North American companies
as a percentage of worldwide pharmaceutical firms has declined from 65% in 2000 to
42% in 2014. Asia has grown significantly, experiencing a 1,030% growth rate from
2000 to 2014, or a 16.8% compounded annual growth rate. From Figure 3.1, it can
also be seen that in terms of market capitalization, Western European companies
28
are the largest, followed by Eastern Europe, North America, then Asia. The average
size of North American companies decreased from around $6.5 billion in 2000 to
approximately $4.2 billion in 2014. It should be noted that all regions saw a shock to
equity markets in 2008, explaining the large shock during that time frame. In general,
the global pharmaceutical and biotechnology industry has grown at around 10% in
terms of market capitalization, and 8.5% in terms of number of companies.
Figure 3.1: Shift in global demographics for the Biotech/Pharma industry.
Shareholder returns are seen to fluctuate quite significantly over the course of
the 21st century with a high correlation of 0.836 with the overall market (see Figure
3.2). Mean returns across the 15 year period was an annual 20.4% with a standard
deviation of 29.6%. While there may not be any discernible positive or negative
pattern in returns, after controlling for market returns, there may be significance
in the shareholder defined R&D productivity both across time and with regards to
M&A activity. Analysis of shareholder defined productivity would reflect the expected
return from each dollar of R&D expense and how M&A activity affects this figure.
29
Figure 3.2: Returns for Biotech/Pharma companies. Correlation with market=0.836.
R&D expenditure has been steadily increasing across time, though this trend saw
a steep decline in 2012 of -16.5%. Expenditure has since continued to increase to
all-time highs. The decline in 2012 parallels a decline in industry average net income
and revenues during the same time period. Average profit margins for the industry
declined initally in the early 21st century but has somewhat grown since 2010 with an
average profit margin of 18% in 2000 and 8.3% in 2008, before recovering to 10% in
2014. Trailing net income growth compared to R&D expense corroborates the theory
that R&D productivity has been declining over time and companies have begun to
seek less profitable alternatives to compensate for rising costs. These trends can be
seen in Figure 3.3.
30
Figure 3.3: Fundamental Data for Pharma/Biotechs Across Time. Recently, R&Dexpenditure growth has outpaced sales growth and net income growth.
Figure 3.4: NME FDA Approvals from 2000-2014
31
3.2.2 NME FDA Approval Data
NME approval data were obtained every year from 2000-2014 from the “New Molec-
ular Entity (NME) Drug and New Biologic Approvals” section of the NDA and BLA
Approval Reports available on the FDA.gov website. The number of NME approvals
per year was assigned to their respective companies and year in the data set. Data
excludes NME approvals acquired by private companies. For name changes that oc-
curred due to acquisitions of a specific year, the FDA approval was recorded under
the acquiror name as reported by Bloomberg. Confirming earlier cited literature,
Figure 3.4 shows that the trend in FDA approvals was fairly stable with a large spike
in 2012 and 2014. This corresponded to a very large increase in the number of NME
applications filed as well (CDE&R, 2013-2015).
Figure 3.5 shows the trend in NME FDA approvals per billion USD of R&D
expenditures. The trend seen is similar to the trend shown by Scannell (2012). There
are differences in the resulting numbers assigned to each year due to the disparities
in data sets as Scannell (2012) utilizes detailed data of only 56 companies provided
by the Pharmaceutical Research and Manufacturers of America organization. Even
though it has become more expensive to create a new drug, the benefits of FDA
approvals have also somewhat increased. As shown in Figure 3.6, both revenues and
profits per FDA approval have been increasing, with the exception of 2008, with
maximum profitability in 2011.
3.2.3 M&A Data
M&A transactions over $1 million USD that closed from 2000 to 2014 were obtained
from SDC M&A. These transactions, both value and quantity, were cross-matched
with company data obtained from the aforementioned sources. A total of 29,047
transactions were recorded. Among these, 538 were in the biopharma space. It
should be noted that a significant portion of health care deals are comprised of
32
Figure 3.5: R&D Productivity as measured by NME approvals from 2000-2014
Figure 3.6: NME profitability (as defined by #NME/Net Income) from 2000-2014
33
services and medtech companies which are categorized differently from life sciences.
Figure 3.7 shows the increasing quantity of M&A activity from 2000-2014. Between
these years, the volume of M&A transactions that have occurred in the biopharma
space has increased from $426.1 million to $754.0 million per transaction while the
average number of transactions increased from 1.17 to 1.42 per company that pursues
M&A. The high value for 2009 was caused by two mega deals: Pfizer’s $67.3 billion
acquisition of Wyeth and Mercks’ $38.6 billion acquisition of Schering-Plough.
Figure 3.7: M&A Acquisitions (in Volume) from 2000-2014 in the Biopharma Industry
34
3.2.4 Summary Statistics and Correlation Matrix
Table 3.2: Summary Statistics for Relevant Variables
Variable Mean Std. Dev. NR&D Expenditure 2,437,126,801 15,278,979,473 9,705
Market Capitalization 2,871,367,860 15,385,419,032 9,705
Sales 863,878,809 4,484,606,555 9,705
Net Income 110,334,344 867,000,453 9,705
ROIC -32.6% 86.5% 9,705
Shareholder Returns 20.48% 110.9% 9,705
Number of NME Approvals 0.022 0.169 9,705
Acquisition Volume ($ in mm) 55.19 967.6 9,705
Number of Acquisitions 0.073 0.356 9,705
Table 3.3: Correlation Matrix for Fundamental ParametersVars M Cap Sales R&D ROIC Returns NME # Acq Acq Vol
M Cap 1
Sales 0.91∗∗∗ 1
R&D 0.12∗∗∗ 0.19∗∗∗ 1
ROIC 0.01∗∗∗ 0.01∗∗∗ 0.01∗∗∗ 1
Returns 0.003 -0.02 -0.01 0.00 1
NME 0.41∗∗∗ 0.39∗∗∗ 0.14∗∗∗ 0.03∗∗∗ 0.02 1
# Acq 0.32∗∗∗ 0.30∗∗∗ 0.01 0.05∗∗∗ -0.02 0.10∗∗∗ 1
Acq Vol 0.28∗∗∗ 0.28∗∗∗ 0.04∗∗∗ 0.03∗∗ -0.01 0.06∗∗∗ 0.30∗∗∗ 1
* p < 0.05, ** p < 0.01, *** p < 0.001
35
Chapter 4
Results
4.1 Effects of M&A on R&D Productivity
4.1.1 General Analysis
Part 1 of the analysis investigates the impacts of M&A on a firm’s R&D productivity
(equations 3.8 - 3.11). We ran regressions to examine the significance of the num-
ber of acquisitions and total M&A volume on NME-based and returns-based R&D
productivity parameters. Figures 4.1-4.4 give an idea as to the nature of the rela-
tionship between these various parameters by plotting the lines of best fit for NME
and shareholder returns defined R&D productivity vs. unlagged, one year lagged,
two year lagged, and three year lagged M&A number and volume from 2000-2014.
Regression Tables 4.1-4.4 quantify the significance of such relationships after cluster-
ing based on geography and controlling for market returns and fundamental financial
data. Year dummies and dummies corresponding to the sub-industry of the firms are
also included, but not presented in the table.
In general, across all parameters, there appears to be a slight negative associa-
tion between R&D productivity and M&A activity. Figures 4.1 and 4.2 define R&D
productivity as the number of NME FDA approvals per billions of dollars of R&D
36
expense. Both diagrams exhibit negative trends for all lags indicating that contrary
to anecdotal evidence and management language, increased M&A activity is generally
associated with lower future R&D productivity. Beyond a certain threshold, specif-
ically around three acquisitions or around $2 billion in volume, it can be seen that
the unlagged and one year lagged M&A variables are more negatively correlated with
R&D productivity than the two and three year lagged variables are. While it may
be interesting to examine the exact time frame in which acquisitions begin to affect
R&D productivity, we leave this to be done for future works.
These diagrams may have several implications. For instance, for high levels of
M&A activity, acquisitions may be associated with lower R&D productivity initially
before realizing slight improvement several years later. Another interpretation may be
that M&A activity simply has no or slight negative effects on R&D productivity and
the negative relationship is due to chance. And finally, the negative relationships for
the unlagged variables may be explained by reverse causation. Namely, companies
that undergo higher numbers and volume of M&A had low R&D productivity to
begin with. It should be noted that in the case of the second explanation, the third
explanation is not necessarily precluded. This is further investigated in Part 2. It is
also important to note that M&A is defined broadly and analysis is applied for all
types of acquirers. While sales and market cap are controlled for in the regression, it
would interesting to apply the analysis to various tranches of firm size. This analysis is
done in the next section. Diagrams summarizing general results are shown in Section
4.3.
Figures 4.3 and 4.4 examine the same question as Figures 4.1 and 4.2 using share-
holder returns defined R&D productivity. While Figure 4.3–which uses the number
of M&A transactions as the dependent variable–does not exhibit any discernible pat-
terns, Figure 4.4 exhibits a clear negative trend comparable to those seen in the first
two figures. Thus there may be evidence that 1. beyond a certain volume in M&A
37
Figure 4.1: R&D Productivity (NME) vs. Total Number of M&A Acquisitions
Figure 4.2: R&D Productivity (NME) vs. Total M&A Volume ($ mm)
38
undergone in one year (around $2 billion), firms have to spend more in R&D to main-
tain the same level of returns, 2. due to other factors, higher levels of M&A spending
is correlated with lower returns, or 3. there is again a case of reverse causation, that
low levels of returns-based R&D productivity induces higher levels of M&A activity.
This third explanation is explored further in Part 2 of this chapter.
Tables 4.1 and 4.2 are the regression tabular outputs for Figures 4.1 and 4.2. The
pattern of coefficients for the acquisition variables is generally consistent with the
trends observed in Figures 4.1 and 4.2. Though insignificant, we find a slight neg-
ative relationship between both the total volume spent and number of acquisitions
made by a firm and NME defined R&D productivity. This implies that after fac-
tors are controlled for, M&A generally does not have a significant effect on a firm’s
R&D productivity within the next three years. Intuitively, the fundamental financial
variables in almost every case significantly correlated with the R&D productivity.
Since the average ROIC amongst biotech and pharmaceutical companies is negative,
it makes sense that the coefficient may take on negative values as well. While the
coefficient of sales is also negative, that of market cap is positive. This implies that
while high sales in one period may not be positively predictive of NME approval in
the future, especially since FDA approvals are relatively rare even for large phar-
maceuticals, market cap may be predictive via stock price fluctuations as investors
anticipate the nearing of an approval. Moving from regressions (1) to (4) in Tables
4.1 and 4.2, it can be seen that the more lagged a variable is, the more positive the
coefficient. Albeit insignificant (p>0.05), such findings corroborate earlier diagrams.
Tables 4.3 and 4.4 are the regression outputs for Figures 4.3 and 4.4. Unsurpris-
ingly, market returns significantly explain returns defined R&D productivity though
fundamental financial data do not. While Figure 4.3 revealed mixed results, once
market returns were controlled for, a clear negative and insignificant relationship
with acquisition volume (Table 4.3) and number (Table 4.4) is evident.
39
Figure 4.3: R&D Productivity (Returns) vs. Total Number of M&A Acquisitions
Figure 4.4: R&D Productivity (Returns) vs. Total M&A Volume ($ mm)
40
Table 4.1: Effect of Acq Vol on NME R&D Prod.
(1) (2) (3) (4)NME RDP NME RDP NME RDP NME RDP
Sales -1.47e-11∗∗∗ -1.85e-11∗∗∗ -1.95e-11∗∗∗ -2.01e-11∗∗∗
(-4.08) (-4.83) (-4.80) (-4.69)
Market Cap 4.20e-12∗∗∗ 5.69e-12∗∗∗ 6.31e-12∗∗∗ 6.35e-12∗∗∗
(3.45) (5.02) (5.00) (4.92)
ROIC -0.00133∗ -0.00145 -0.00174∗ -0.00133(-2.25) (-1.87) (-2.00) (-1.47)
GDP -0.0000369 -0.0000173 -0.0000359 -0.0000183(-1.21) (-0.75) (-1.14) (-0.70)
Acq Volume -0.000000354(-0.36)
L.Acq Volume -0.000000453(-0.29)
L2.Acq Volume -0.000000349(-0.15)
L3.Acq Volume -0.00000347(-0.03)
Constant 0.723 0.433 0.691 0.445(1.57) (1.27) (1.46) (1.14)
Observations 9705 8101 6830 5800
t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
41
Table 4.2: Effect of Acq Num on NME R&D Prod.
(1) (2) (3) (4)NME RDP NME RDP NME RDP NME RDP
Sales -1.49e-11∗∗∗ -1.90e-11∗∗∗ -1.91e-11∗∗∗ -2.00e-11∗∗∗
(-4.11) (-4.94) (-4.66) (-4.69)
Market Cap 3.94e-12∗∗ 5.32e-12∗∗∗ 6.77e-12∗∗∗ 6.29e-12∗∗∗
(3.15) (4.43) (5.13) (4.64)
ROIC -0.00133∗ -0.00146 -0.00173∗ -0.00133(-2.26) (-1.88) (-2.00) (-1.47)
GDP -0.0000366 -0.0000166 -0.0000367 -0.0000181(-1.19) (-0.71) (-1.16) (-0.69)
Acq Num -0.0950(-1.40)
L.Acq Num -0.0879(-0.75)
L2.Acq Num -0.0604(-0.33)
L3.Acq Num -0.0436(-0.24)
Constant 0.716 0.418 0.708 0.442(1.54) (1.22) (1.48) (1.13)
Observations 9705 8101 6830 5800
t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
42
Table 4.3: Effect of Acq Vol on Return R&D Prod.
(1) (2) (3) (4)Ret RDP Ret RDP Ret RDP Ret RDP
Mkt Returns 0.00000147∗∗ 0.00000166∗∗ 0.00000155∗∗ 0.00000171∗
(2.82) (2.95) (2.64) (2.57)
Sales -1.60e-16 -4.35e-16 -4.28e-16 -1.95e-16(-0.30) (-0.75) (-0.82) (-0.36)
Market Cap -1.50e-16 -2.12e-16 -2.18e-16 -3.13e-16(-1.18) (-1.55) (-1.61) (-1.90)
ROIC -0.000000103 4.71e-08 2.85e-08 1.12e-08(-0.43) (0.16) (0.08) (0.03)
GDP -8.54e-09 -9.70e-09 -9.05e-09 -1.24e-08(-1.45) (-1.21) (-0.82) (-0.76)
Acq Volume -2.41e-10(-1.25)
L.Acq Volume -3.58e-10(-1.10)
L2.Acq Volume -4.73e-10(-1.18)
L3. Acq Volume -1.13e-10(-0.25)
Constant 0.000129 0.000153 0.000145 0.000196(1.52) (1.33) (0.90) (0.79)
Observations 9705 8101 6830 5800
t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
43
Table 4.4: Effect of Acq Num on Return R&D Prod.
(1) (2) (3) (4)Ret RDP Ret RDP Ret RDP Ret RDP
Mkt Returns 0.00000147∗∗ 0.00000166∗∗ 0.00000155∗∗ 0.00000171∗
(2.82) (2.95) (2.64) (2.57)
Sales -1.30e-16 -3.30e-16 -3.28e-16 -1.37e-16(-0.24) (-0.56) (-0.61) (-0.24)
Market Cap -9.24e-17 -1.52e-16 -1.44e-16 -2.36e-16(-0.90) (-1.38) (-1.41) (-1.82)
ROIC -0.000000102 4.83e-08 2.94e-08 1.18e-08(-0.42) (0.16) (0.09) (0.03)
GDP -8.63e-09 -9.82e-09 -9.18e-09 -1.25e-08(-1.45) (-1.22) (-0.83) (-0.76)
Acq Num -0.00000967(-1.75)
L.Acq Num -0.0000112(-1.85)
L2.Acq Num -0.0000114(-1.53)
L3.Acq Num -0.0000112(-1.30)
Constant 0.000130 0.000155 0.000148 0.000198(1.53) (1.34) (0.91) (0.80)
Observations 9705 8101 6830 5800
t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
44
4.1.2 Analysis by Size
As has been cited in anecdotal accounts, analysis involving the break down of firm
size may be useful to further understand this finding. Already from regression Tables
4.1 and 4.2, it can be seen that the coefficient of the sales variable is negative and
significant, possibly implying and confirming existing empirical research that the
larger the firm, the worse the R&D productivity post-acquisition. Figures 4.5 and
4.6 show the relationship between NME R&D productivity and M&A activity based
on size. Acquirers are separated into two groups: small caps which have a market
capitalization of under $2 billion and large caps which have a market capitalization
over $2 billion.
Figure 4.5: R&D Productivity (NME) vs. Number of M&A Acquisitions by Size
45
Figure 4.6: R&D Productivity (NME) vs. M&A Volume ($ mm) by Size
These diagrams reveal a clear difference in the correlation between R&D Pro-
ductivity and level of M&A activity. While large caps have a negative association
between R&D productivity and M&A activity, small caps have a positive one. In
other words, M&A tends to have a negative effect on the R&D productivity for larger
firms and a slight positive effect on smaller firms. Further regressions are carried out
below to isolate the effect of size by controlling for fundamental financial variables and
geographic, sub-industry, and yearly fixed effects. In Tables 4.5 and 4.6, regressions
(1) and (2) correspond to large cap firms while regressions (3) and (4) correspond to
small cap firms.
From these regressions, though the majority of coefficients in front of the acquisi-
tion variable remain insignificant, we see positive coefficients for each of the small cap
regression and a negative coefficient for each of the large cap regressions. Statistical
significance is observed for the unlagged acquisition variables for large caps, implying
46
Table 4.5: Effect of Acq Vol on NME R&D Prod. by Size
(1) (2) (3) (4)Large RDP Large RDP Small RDP Small RDP
Sales -1.11e-11∗∗∗ -1.28e-11∗∗∗ -1.64e-10∗ -1.60e-10∗
(-3.40) (-3.45) (-2.29) (-2.01)
Market Cap 3.89e-12∗∗ 4.29e-12∗∗ 4.60e-10∗∗∗ 4.64e-10∗∗∗
(2.79) (3.18) (3.85) (3.49)
ROIC -0.0155 -0.0160 -0.00149∗ -0.00165∗
(-1.35) (-1.30) (-2.57) (-2.14)
GDP -0.0000229 -0.0000433∗ -0.0000470 -0.0000214(-1.49) (-2.16) (-1.35) (-0.80)
Acq Vol -0.00001750∗ 0.000607(-2.25) (1.14)
L.Acq Vol -0.00000568 0.0000417(-1.17) (0.12)
Constant 0.683∗∗ 1.007∗∗ 0.734 0.350(2.62) (2.91) (1.38) (0.88)
Observations 1193 1059 8512 7042
t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
that for larger firms, M&A as a whole tends to decrease the R&D productivity of
the firm. M&A for smaller firms tend to result in higher levels of R&D productiv-
ity, though this relationship is largely insignificant across all specifications and lags
of M&A. Thus, while general analysis showed an insignificant, negative relationship
between M&A and its effects on R&D productivity, this negative relationship holds
significantly for large firms while smaller firms tend to experience an opposite positive,
insignificant effect on its productivity from M&A.
4.2 Effects of R&D Productivity on M&A
The second part of the analysis investigates the effects of R&D productivity on the
likelihood of undergoing M&A activity (equations 3.12-3.15). A quasi-experimental
47
Table 4.6: Effect of Acq Num on NME R&D Prod. by Size
(1) (2) (3) (4)NME RDP NME RDP NME RDP NME RDP
Sales -1.11e-11∗∗∗ -1.32e-11∗∗∗ -1.64e-10∗ -1.59e-10∗
(-3.37) (-3.65) (-2.27) (-2.00)
Market Cap 4.10e-12∗∗ 4.50e-12∗∗ 4.43e-10∗∗∗ 4.56e-10∗∗∗
(2.77) (3.16) (3.67) (3.39)
ROIC -0.0156 -0.0160 -0.00149∗ -0.00164∗
(-1.35) (-1.30) (-2.55) (-2.13)
GDP -0.0000247 -0.0000446∗ -0.0000462 -0.0000203(-1.59) (-2.18) (-1.32) (-0.76)
Acq Num -0.0690∗ 0.0331(-2.17) (0.14)
L.Acq Num -0.0370 0.122(-1.11) (0.46)
Constant 0.720∗∗ 1.034∗∗ 0.724 0.332(2.68) (2.89) (1.35) (0.83)
Observations 1193 1059 8512 7042
t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
design is used in which all firms are grouped by R&D productivity into a top 50
percentile and bottom 50 percentile bucket, i.e. a dummy variable is created to mark
the bottom 50% of firms with the lowest NME and returns defined R&D productivity.
As mentioned earlier, such a method controls for macro factors, such as the incentives
to pursue M&A for inversion purposes and the operating environment induced by the
Affordable Care Act. After controlling for fundamental factors, such a method will
show the association between R&D productivity and its effects on M&A activity.
Regressions are run to examine the significance of NME and returns defined R&D
productivity on the number and volume of acquisitions a firm undertakes. Unlagged
and one year lagged R&D productivity variables are included in this analysis.
Figures 4.7 and 4.8 convey the relationship between these parameters across time.
The lines of best fit are plotted for M&A activity from 2000-2014 for the two tranches
48
of R&D productivity. In general, for both number and volume of M&A, firms with
lower R&D productivity undergo high levels of M&A activity. Confirming previously
cited literature, the sheer number of acquisitions per firm has declined across the
years, but the total volume has increased across all firms.
Figure 4.7 below not only shows the declining number of total M&A per firm
across time for firms that pursue M&A activity, but that such declines are decreas-
ing more for companies with higher R&D productivity, indicating that those with
low productivity in general have pursued a greater number of acquisitions over time
than their high R&D productivity counterparts. Though the declining nature of the
number of acquisitions completed per firm is not addressed in this paper, it would be
interesting to examine this trend in future works.
Figure 4.8 shows the total M&A volume for the two groups of R&D productivity
from 2000-2014. In addition to the increasing volume trend across time, we find
that generally, firms in the top 50% R&D productivity tranche undertook less M&A
activity than those with lower R&D productivity. Together, Figures 4.7 and 4.8 may
be evidence of a relationship between lower R&D productivity and increased M&A
activity.
49
Figure 4.7: Number of Acquisitions Across Time by R&D Productivity Level
Figure 4.8: Acquisition Volume Across Time by R&D Productivity Level
50
Tables 4.5-4.8 are the regression tabular outputs for Figures 4.7 and 4.8. Tables
4.5 and 4.6 regress NME-based R&D productivity while Tables 4.7 and 4.8 regress
shareholder returns-based R&D productivity on M&A activity. Regressions 1 and 2
in all four tables regress R&D productivity, the bottom 50% dummy variable, and
controls on the acquisition variable. Regressions 3 and 4 display the same regressions
using one year lagged R&D productivity and bottom 50% dummy variables. The
coefficients of interest are those on the R&D productivity and 50% dummy variables.
Again, the regressions cluster based on geography and control for market returns and
the fundamental financial variables of sales, market cap, ROIC, and GDP. Year and
sub-industry dummies are also included but not presented in the tables. We find that
the pattern of coefficients on the unlagged R&D productivity variables to be fairly
consistent with trends in Tables 4.5 and 4.6. Specifically, the lagged R&D productivity
was negatively and significantly associated with levels of M&A activity. Consistent
with the results found in the previous section, unlagged R&D productivity was not
significantly associated with M&A activity. This may imply that pharmaceutical
companies with lower R&D productivity turn to M&A as a strategic alternative more
so than companies with already high R&D productivity. The coefficients on the
bottom 50% RDP variables tended to be positive and significant, providing strong
evidence of a higher pursuit of M&A activity among less productive firms.
Interestingly, the fundamental financial variables in nearly every regression were
not statistically significant in explaining M&A activity, implying that M&A decisions
may either not be dependent on the financial performance of the company or may
be subject to several conflicting forces (e.g. firms with better operating performance
may seek acquisitions for expansion purposes while poorly performing firms would
acquire for defensive purposes). For the most part, market returns were not signifi-
cantly correlated with M&A activity. This may be due to the fact that there are two
conflicting forces: strong market performance gives a company greater purchasing
51
power and thus is more likely to pursue an acquisition while weak market perfor-
mance puts competitive pressure on a firm, making consolidation also more likely
(Danzon, 2007). Furthermore, we find that the lagged variables are also, for the most
part, significant. This implies some temporal association between the two variables,
suggesting that low R&D productivity precedes and is more likely a determinant of
M&A strategy. It should be noted that Tables 4.5-4.6 have relatively few observations
for each regression. This is due to the relatively scarce nature of FDA approvals as
only observations with FDA approvals are considered in the regressions.
Similar trends can be seen in Tables 4.7 and 4.8 which regress returns based
R&D productivity on M&A activity. One large difference is on the coefficients of the
bottom 50% RDP dummy variable which, though positive, are no longer significant.
This implies that less productive firms undertake more M&A activity, but not to
the extent and significance as observed in earlier regressions. This difference may
be due to how R&D productivity is defined in these regressions. Since shareholder
returns are not solely indicative of R&D productivity and reflect all elements of a
business, both low and high shareholder returns defined R&D productivity may be
similarly correlated to M&A activity. In other words, a firm with high shareholder
returns may pursue M&A just as a firm with low shareholder returns, as problems
may not be pipeline-based in nature. Thus, being in the top or bottom 50% of firms
for R&D productivity is not indicative of M&A activity when productivity is returns
defined. Again, market returns and fundamental variables for the most part were not
significant in explaining M&A.
52
Table 4.7: Effect of NME R&D Prod. on Acq Num
(1) (2) (3) (4)Acq Num Acq Num Acq Num Acq Num
Bot 50% RDP 0.284∗∗ 0.245∗
(2.44) (2.02)
NME R&D Prod -0.00287∗ -0.00203(-2.17) (-1.71)
L.Bot 50% RDP 0.385∗∗ 0.293∗
(3.06) (2.23)
L.NME R&D Prod -0.00156∗ -0.00186∗
(-2.06) (-2.24)
Mkt Returns 0.00421 0.00407(0.78) (0.74)
Sales 6.19e-12 -1.28e-12(0.75) (-0.15)
Market Cap 4.46e-12∗ 3.48e-12(1.99) (1.49)
ROIC 0.000261 -0.000482(1.31) (-0.78)
GDP -0.0000170 -0.0000427(-0.90) (-1.27)
Constant 0.495∗∗∗ 0.219 0.577∗∗∗ 0.940(5.41) (0.85) (5.15) (1.72)
Observations 335 335 305 305
t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
53
Table 4.8: Effect of NME R&D Prod. on Acq Vol
(1) (2) (3) (4)Acq Vol Acq Vol Acq Vol Acq Vol
Bot 50% RDP 733.6∗ 711.2∗
(2.53) (2.19)
NME R&D Prod -2.572∗ -1.709(-2.23) (-1.68)
L.Bot 50% RDP 2218.4∗∗ 1662.9(2.55) (1.76)
L.NME R&D Prod -1.617∗ -1.604∗
(-2.02) (-1.97)
Mkt Returns 0.721 1.557(0.16) (0.72)
Sales -4.70e-10 9.30e-08(-0.02) (0.97)
Market Cap 7.31e-10 5.21e-09(0.10) (0.40)
ROIC 0.195 -5.003(0.52) (-1.15)
GDP 0.0702 0.0146(1.79) (0.15)
Constant 872.6∗∗ -176.2 1748.2 -1067.9(3.23) (-0.35) (1.89) (-0.74)
Observations 335 335 305 305
t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
54
Table 4.9: Effect of Return R&D Prod. on Acq Num
(1) (2) (3) (4)Acq Num Acq Num Acq Num Acq Num
Bot 50% RDP 0.0240∗ 0.0187(2.24) (1.25)
Returns R&D Prod -1.803∗ -1.176(-2.07) (-1.03)
L.Bot 50% RDP 0.0227∗ 0.00799(2.27) (0.97)
L.Returns R&D Prod -4.383∗∗ -2.351∗
(-2.74) (-1.96)
Mkt Returns 0.00721 -0.00557(0.95) (-0.42)
Sales 3.85e-12 -2.44e-12(0.71) (-0.46)
Market cap 6.20e-12∗∗∗ 8.50e-12∗∗∗
(3.73) (4.89)
ROIC 0.0000574∗ 0.0000595(2.26) (1.91)
GDP -0.00000943∗∗∗ -0.0000122∗∗∗
(-5.62) (-5.57)
Constant 0.0631∗∗∗ 0.185∗∗∗ 0.0713∗∗∗ 0.231∗∗∗
(13.82) (7.44) (12.95) (7.12)
Observations 9705 9705 8080 8080
t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
55
Table 4.10: Effect of Return R&D Prod. on Acq Vol
(1) (2) (3) (4)Acq Vol Acq Vol Acq Vol Acq Vol
Bot 50% RDP 45.84∗ 31.405(2.20) (1.03)
Returns R&D Prod -2069.4∗ -1529.65(-2.02) (-1.54)
L.Bot 50% RDP 31.18∗ 18.09(2.28) (0.97)
L.Returns R&D Prod -3725.2∗ -3308.7∗
(-2.14) (-1.97)
Mkt Returns 1.652 1.528(0.56) (0.42)
Sales 2.83e-08 1.22e-08(1.07) (0.45)
Market Cap 1.00e-08 1.62e-08∗
(1.74) (2.26)
ROIC -0.0340 -0.0245(-0.53) (-0.42)
GDP 0.00102 -0.000273(0.42) (-0.09)
Constant 35.33∗∗∗ -16.93 55.55∗∗ 14.65(4.74) (-0.46) (2.81) (0.31)
Observations 9705 9705 8080 8080
t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
56
4.3 Diagram Summaries of Results
Below are several simplistic diagrams that capture the main findings in this section
and summarize the time frame, strength, and direction of the relationship between
R&D productivity and M&A activity.
Figure 4.9: General M&A Results. M&A activity tended to be slightly negatively
and insignificantly associated with later R&D productivity across 0-3 year lags.
Figure 4.10: M&A Results by Size. In general, M&A activity for smaller firms of
under $2 billion in market cap had a slightly positive but insignificant association
with later R&D productivity. Large firms had a negative and significant association.
57
Figure 4.11: General R&D Results. In general, firms with lower R&D productiv-
ity pursued a greater number and volume of acquisitions within the next two years
compared to firms with higher levels of R&D productivity.
Figure 4.12: Combined R&D Productivity and M&A Results. Combining earlier
findings, large firms with low R&D productivity are more likely to pursue acquisi-
tions though generally, these acquisitions do not yield benefits to the firms’ R&D
productivity.
58
Chapter 5
Discussion
5.1 M&A and R&D Productivity Relationship
The pharmaceutical industry provides a good laboratory to investigate the effects of
mergers and acquisitions on innovation and R&D productivity. Before all else, this
study confirms that over the past decade, the industry has been characterized by both
significant consolidation of firms in an attempt to, among other objectives, vertically
integrate the R&D process and combat the diminishing returns to R&D productivity.
With increasing R&D spend and declining annual FDA approvals, M&A has been
often sought as a strategic move to improve the positioning of a firm’s pipeline in a
cost effective way. Despite anecdotal evidence and language used by the management
teams of pharmaceutical companies in describing M&A pursuits, concerns have been
brought up questioning the efficacy of M&A as a strategic alternative. Such concerns
were raised after witnessing many large firms, including most recently, Valeant Phar-
maceuticals, failing to convert pipeline related M&A into shareholder returns. While
mergers apparently have achieved cost reductions and addressed short-run pipeline
problems, there is little evidence to date that they increased long-term R&D per-
59
formance or outcomes. Many of the large pharmaceutical firms listed in Table 1.1
continue to deal with persistent issues in their R&D productivity (Grabowski, 2008).
Our study uses panel data and OLS techniques to deeper understand this issue
from an academic context. Confirming aforementioned concerns, we obtain insignifi-
cant results when using M&A activity to explain R&D productivity. Similar results
are seen when using lagged M&A variables to address the issue of reverse causation.
This implies that general M&A activity across all firm sizes does not bring about
higher levels of R&D productivity in the future. Such a finding may come with a
slight qualification, however. Since M&A is defined generally as any acquisition or
merger, this would encompass transactions for poorly performing firms where con-
solidation is related more to survival than pipeline-related strategy. Thus, this may
artificially depress R&D productivity in the future as the consolidated firm may con-
tinue to decline. There is evidence that firms under economic stress are more likely to
engage in mergers. An often cited firm-specific motivation for pharmaceutical M&A
is to vertically integrate a company’s pipeline to fill in any gaps to maintain growth in
the face of a major product’s patent expiration. Patent expiration on legacy drugs can
result in rapid losses in unit sales as generic entrants flood the market and leave firms
with significant excess capacity in their sales and marketing divisions (Grabowski et
al., 2002). As a result, it would be interesting to address the question of whether
M&A improves the R&D productivity of firms actively seeking to improve it. To
further investigate this finding, we would need a proxy for strategic pipeline-related
acquisitions, which could only be done accurately with project-level R&D data and
transaction-level M&A data. Though such correction would most likely yield a neg-
ligible difference in the final result of this study (Grabowski & Kyle, 2008), a further
break down of the M&A variable would yield interesting analysis in future works.
This finding also raises the question of why firms would pursue M&A in the first
place if such behavior is generally detrimental to R&D productivity. One possible
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explanation may be that M&A does not result in the intended consequences. While
mergers often look promising in theory, a poorly planned integration process and
other unforeseen hurdles such as cultural conflicts, may often result in unanticipated
failures in practice (Bekier et al., 2001). Another explanation may be that there
exist other reasons for M&A other than improving R&D productivity. While we
showed that the declining R&D productivity in the industry is a significant factor
in explaining M&A activity, such a finding, while it controls for, does not preclude
other factors in also explaining M&A. One such factor may be possible savings in an
inversion deal. While the mergers realize significant tax savings, R&D expense could
increase upon the combination of the two R&D programs; FDA approvals continue to
falter as innovation becomes increasingly difficult, thus lowering R&D productivity.
As referenced in Chapter 4, the level of significance also appears to be based on
size. Namely, while scope of drug pipeline is not considered, it was found that while
large (over $2 billion market cap) pharmaceutical companies tend to experience either
negative or insignificant effects of M&A on their R&D productivity, smaller firms tend
to yield a somewhat positive albeit insignificant gain in R&D productivity. This may
be due to the many benefits smaller firms experience over larger firms such as being
closer to cutting edge technology emerging from universities and public-supported
basic research, being more willing to take risks on disruptive technologies, and being
less bureaucratic in organizational structure (Scherer, 1999). To expand on this,
since drug discovery by nature is a highly speculative, time-consuming, and costly
venture, large organizations are more wary towards risky projects, such as developing
a disruptive but low PoS drug, which could sink a steady ship if not carefully managed.
However, this leads to large pharma organizations being frequently plagued with
bureaucratic red tape across all verticals that increases cost structure and limits the
scope of projects that may be taken on. Mark Levin of Third Rock Ventures said it
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often took 18-24 months for a biotech start up to negotiate a partnership with a Big
Pharma company, due to bureaucracy.
A study by BCG found that the cumulative effect of bureaucratic mechanisms
such as key performance indicators not only slowed down the R&D process and in-
creased costs, but also degraded the quality of strategic decision-making and reduced
collaboration as employees engaged in rent-seeking behavior. One employee survey
at a large biopharma firm revealed that 2/3 of the R&D team would put their de-
partmental and personal interests above those of the company’s as a whole and over
70% of employees found the organization’s decision-making process ineffective. Such
bureaucracy, though still somewhat applicable, is more limited at smaller firms. The
study concluded that increased bureaucracy at pharma firms led to a large disconnect
between personal motivation and firm-wide interests which ”drives a significant share
of the poor productivity in the industry” (BCG, 2011). Such a finding, combined
with general merger theory, may explain the negative association between large cap
M&A activity and decreased R&D productivity in the long term compared to small
cap M&A.
In Part 2, the study analyzes the reverse relationship: the effect of R&D produc-
tivity on likeliness to pursue M&A opportunities. Across all specifications of M&A
activity and R&D productivity, initial yearly cross-sectional regressions reveal a nega-
tive but insignificant association when regressing R&D productivity on M&A activity
after controlling for outside factors. This is consistent with the results found in Part
1. The lagged variables, however, were found to be significant and imply a certain
degree of temporal priority that suggests the direction of causation. Namely, a low
R&D productivity is significant in explaining a later decision to pursue M&A activity
while the reverse is not. The similar results across the multiple specifications of R&D
productivity and M&A activity with insignificant constant terms in each regression
show a degree of robustness in these findings and imply that while low R&D produc-
62
tivity is significant in explaining a firm’s acquisitive behavior, general M&A tends to
yield either slightly lower or no significant R&D productivity changes.
Regressions in Part 2 control for financials, implying that on top of any impacts of
poor financial performance, a decision to pursue M&A is significantly associated with
its current R&D productivity. The 50% R&D productivity dummy serves to control
for other non-deal specific operating environment factors during this time frame that
may influence a decision to pursue M&A. One criticism may be that these models do
not account for deal-specific synergies unrelated to the pipeline, such as cost savings.
However, such deal-specific synergies may be constituted as productivity towards
enhancing the firm as a whole–synergies ultimately serve the purpose of reducing drug
output costs and facilitating the drug discovery and creation process. Furthermore,
the synergies are reflected in the shareholder returns and as a result, our definitions
of R&D productivity. Thus the controlling for these reasons may not be necessary
and would likely not meaningfully affect our findings.
In general, these findings confirm the most recent empirical and anecdotal evidence
of the R&D productivity trend. The findings support and build upon the relatively
sparse and outdated academic literature in this space that utilized different research
methods and were conducted over 10 years ago. Compared to existing literature, this
study includes a much wider data set of mergers, utilizes an approval-based R&D
productivity, and analyzes a new era of life sciences developments and mergers.
5.2 Policy Implications
With these findings, several policy implications can be made. At the investor level,
one should approach the life sciences industry with caution, especially when evaluat-
ing optimistic announcements by big pharmaceutical firms regarding M&A that paint
a rosy picture of pro-forma pipeline-related enhancements. While some mergers do
63
yield higher long-term productivity, each investment will need to be done on a case-
by-case basis with attentive regards to the purchase price, PoS of clinical trials, and
degree of overlap of pipelines to realize expertise-sharing and cost-cutting synergies.
From a private equity perspective, large buyouts in attempt to improve the operations
of a pipeline suffering biotech may require a strategy beyond simply acquiring other
firms. On another level, such investors may even look to target large pharmaceutical
companies actively pursuing acquisitions to supplant their pipeline. If not experi-
encing the R&D productivity crisis within their own firms, managers would need to
recognize the declining trend of R&D productivity in the industry and account for
potential acquisition attempts. Furthermore, for certain firms, attempts to improve
R&D productivity in the face of diminishing in-house innovation may need to extend
beyond simply M&A. Again, a firm-by-firm analysis would be necessary.
At the government level, a watchful eye needs to be kept on the industry from an
antitrust perspective. Since 2013, at least three of the largest ten acquisitions in the
world occurred in the life sciences space alone including the largest life sciences deal
to date: Pfizer’s $160 billion acquisition of Allergan. Especially with the tendency
to inflate drug prices, fewer producers of drugs and declining R&D productivity may
have severe repercussions for the general population if antitrust oversight of the in-
dustry is not put in place. One of the biggest antitrust concerns for R&D intensive
pharmaceutical firms is in the area of innovation markets. In particular, this issue
arises when two merging firms have highly similar drug candidates in their pipelines.
The merger could result in the suppressing of one of the research paths to maximize
the economic performance of the other candidate once FDA approved at the expense
of the customer. To date, there have been ten challenges for mergers in innovation
markets, eight of which have involved the biopharmaceutical industry (Carrier, 2008).
Furthermore, recognition of the declining R&D productivity trend may allow gov-
ernments to incentivize the private sector to create drugs that address relatively
64
unprofitable but important diseases. Every year, future vaccines for the flu and other
emerging infections are a major priority for global health, yet production is extremely
limited due to little reward. Manufacturers produce a predetermined quantity based
on how much they expect to sell. Thus, there exists an inability to rapidly expand
vaccine supply in times of need. For example, due to the slow production process,
sufficient quantities of the vaccines against the 2009-2010 swine flu became available
only after the outbreak had subsided. Months later, only a fraction of the doses made
it to the developing world. It was estimated that swine flu killed as many as 575,400
people globally during this time (Centers for Disease Control and Prevention, 2012).
Thus, given the market reality and the declining R&D productivity in the industry, it
may be an opportune time for the government to get private, large pharmaceuticals
more deeply involved. Several possibilities that may be considered include tax credits
for R&D spend, fast-track procedures for relevant product approval, and extensions
for patents and periods of market exclusivity. By engaging big pharma to create
future vaccines, governments can ensure that a market failure would not lead to a
public health catastrophe.
5.3 Limitations and Future Work
While FDA NME approvals do not reflect the intermediary R&D advancements that
a firm may experience, shareholder returns reflect all advancements and other non-
pipeline related factors that affect the firm. If not faced with resource constraints,
future works would ideally create a R&D productivity data set that spans a large
number of biotech companies, their expenditure on R&D related projects, and these
outcomes. Outcomes would be defined not just by success or failure, but also by the
amount of economic returns achieved over a specific time frame.
65
Furthermore, while this study looks at both M&A volume and the number of
transactions, it does not track partnerships across firms that may serve similar pur-
poses as M&A from a pipeline perspective. Collecting such a data set would be a
time-consuming process but analysis on such data would be interesting and would
complement the results found in this study. And though three years may already be
sufficient, lagging M&A variables for longer intervals may yield other useful findings.
Finally, though this study establishes the direction and significance between M&A
activity and R&D productivity, future work may further break down M&A and R&D
productivity to identify which aspects of the two are the major determinants. For
example, is the propensity to merge a result of weak R&D spending, a lack of expertise
in certain sub-industries, or some other cause of diminished ability to innovate? On
the other hand, is R&D productivity improved or worsened with offensive versus
defensive M&A transactions? It may also be interesting to delve deeper into the
causes for the effects of M&A on R&D productivity based on size. One such method
may be to use a proxy for bureaucracy to analyze its effects on pipeline developments.
And though briefly done in this study, time series analysis of M&A and its effects on
R&D productivity may also be another possible area for future research.
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Chapter 6
Conclusion
A comprehensive and detailed panel data set for biopharmaceutical firm merger,
financial, and R&D productivity has been compiled. Next, existing empirical and
academic research on rising rates of M&A activity and declining R&D productivity
in the biopharma space have been confirmed. Using several multi-factor models with
0-3 years of lag, we found that after controlling for financial fundamental data and
other fixed effects, a lower level of R&D productivity is significantly associated with
higher, later levels of M&A activity though M&A activity in general does not impact
the R&D productivity of the acquirer. A further breakdown of acquirer size reveals
that large cap firms experience worse R&D productivity pro forma compared to small
caps. Among other explanations, M&A may look promising in theory but then have
unintended consequences once executed. And though low R&D productivity may
be one explanation, M&A may also be pursued due to other factors that increases
total R&D spend and thus lowers NME defined R&D productivity. As the first
study to use large swathes of pharmaceutical data to analyze R&D productivity and
M&A activity across multiple specifications, this study contributes important findings
to a limited set of academic studies focusing on the relationship between the two
parameters. Future work may include more refined analysis that includes R&D data
67
at the project level and alliances and partnerships across biopharmaceutical firms, in
addition to M&A.
68
I hereby declare that I am the sole author of this thesis.
I authorize Princeton University to lend this thesis to other institutions or in-
dividuals for the purpose of scholarly research.
I further authorize Princeton University to reproduce this thesis by photocopying or
by other means, in total or in part, at the request of other institutions or individuals
for the purpose of scholarly research.
69
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