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
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(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
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(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
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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
t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
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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.
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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.
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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-
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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-
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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
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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
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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.
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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
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at the project level and alliances and partnerships across biopharmaceutical firms, in
addition to M&A.
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I further authorize Princeton University to reproduce this thesis by photocopying or
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