Private Equity and Long Run
Investments: The Case of
Innovation
Josh Lerner, Morten Sorensen,
and Per Stromberg
Motivation
•  We study changes in R&D and innovation for
companies involved in buyout transactions.
•  This helps us distinguish two opposing views of
buyout transactions:
–  Buyouts liberate firms from short-term agency
problems arising in public firms (Jensen [1989]).
–  Buyouts compromise long-term values to extract
short-term rents (Shleifer, Summers [1988]).
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Hypothesis 1: Private Equity
investors as long-term investors
•  Jensen [1989] “The eclipse of the public
corporation” predicts that LBOs will become the
dominant corporate organization form:
–  Superior corporate governance.
–  Concentrated ownership by active owners.
–  Strong managerial incentives.
•  Impact on innovation:
–  Facilitate long-run investments that myopic (quarterto-quarter) public firms cannot.
–  Avoid wasteful expenditures of others (Jensen [1993])
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“I realize, gentlemen, that thirty million dollars is a
lot of money to spend. However, it’s not real
money and, of course, it’s not our money either.”
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Hypothesis 2: Private Equity
investors as short-term investors
•  Critics suggest different view (Shleifer, Summers
[1988]):
–  Investors compromise long-term value creation to
enhance short-term performance.
•  Renege on implicit and explicit obligations to employees and
retirees.
–  This helps investors “flip” offerings quickly, to pay
large dividends:
•  Boosts IRR and allow raising new funds sooner, enhancing
fee income.
•  Implication for innovation is a temptation to defer
expenditures:
–  These depress current accounting earnings with few
immediate gains.
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The Great Global Buyout Bubble, by Andrew Sorkin, New York Times
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What we do
•  We examine investment in innovation as one
form of long-run investment.
•  Investment in innovation presents an attractive
testing ground:
–  Costs must be written off immediately.
–  Benefits may not be apparent for many years.
•  But clearly important to long-term success.
–  Systematic, well-understood measures available.
•  We use patent data to overcome problems with gathering
data for private firms.
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Rationale for study
•  Growth of private equity industry:
–  Larger sample to work with.
–  Investors now have more operational orientation…
but also more intense competition.
•  Recently have observed greater representation
of technology transactions.
•  Patent data allow us to look beyond the “publicto-private” transactions:
–  Private-to-private deals may have different features.
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Relevant Literature (1/2)
•  R&D and capital constraints:
–  Greenwald, Salinger and Stiglitz [1991]: “Case
studies” of auto and airline industries.
–  Hall [1992]: 1247 R&D-performing manufacturing
firms.
–  Hao and Jaffe [1993]: Panel data on 81 firms in five
high-tech industries.
–  Himmelberg and Petersen [1994]: Panel data on 179
small high-tech firms.
•  Conclusions:
–  Internal finance availability important for R&D.
–  Interpretation of pattern is challenging.
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Relevant Literature (2/2)
•  Hall [1992] considers public-to-private LBOs
during 1980s:
–  Notes these firms were doing little R&D before
transaction.
–  4% of 1982 employment, but 1% of R&D.
–  Concludes LBO wave unlikely to have much impact
on innovation.
•  Lichtenberg and Siegel [1990]: 43 whole-firm
LBOs that filled out RD-1 survey
–  R&D expenditures appear to increase after LBO on
absolute and relative basis.
–  More likely for R&D to increase for LBO firms than for
matches.
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Data: Transactions (1/4)
•  Begin with CapitalIQ database:
–  Transactions between 1/1/80 and 12/31/05.
–  Focus on leveraged buyout investments.
•  Classified in CIQ as “Going Private,” “JV/LBO,”
“LBO,” “Management Participated,” or “MBO.”
•  Closed and effective deals.
–  Supplement with information from Dealogic,
other CapitalIQ databases, Directory of
Corporate Affiliations, Hoovers on financial
characteristics, previous parents, exits.
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Data: Match to patents (2/4)
•  Match CIQ data to HBS patent database:
–  Contains of all patents awarded through May
2007.
–  Assignee names have been cleansed (relative
to original USPTO data).
•  Identify all LBO targets assigned at least
one patent from three years prior to five
years after buyout:
–  Match on name and location.
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Data: Divisional targets (3/4)
•  If LBO target is a unit of a larger firm,
patents likely to be assigned to parent.
•  Identify all corporate parents in [-3,+5]
window:
–  CapitalIQ, Dealogic, DCA, Factiva, Google.
•  Identify all patents assigned to parent with
same inventor as LBO target.
•  This method captures some, but not all, of
the relevant parent patents.
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Data: Trimming (4/4)
•  For CapitalIQ buyouts from 1980 to 2005:
–  496 firms have successful patent applications
filed from years -3 to +5 relative to buyout:
•  Due to many recent buyouts
•  Due to “old economy” nature of firms.
–  8,938 patents filed in this window, but >1/4th
assigned to Seagate:
•  Second largest firm has 4%.
•  We eliminate Seagate, leaving us with 6,398
awarded patents.
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Investments and Exits by Year
80
70
60
50
40
Investments
Exits
30
20
10
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
0
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Patents and LBOs in sample
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Patent Applications and Grants
1000
800
600
400
Applications
Grants
200
0
1983 1986 1989 1992 1995 1998 2001 2004 2007
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Industry composition
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Number of patent applications,
relative transaction
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Methodology: three measures of
innovation
•  We measure three aspects of innovation:
–  Patent quality:
•  Economic impact.
•  Basic/fundamental nature.
–  Number of patent filings.
–  Patent portfolio composition.
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Patent quality measures
•  Citation counts:
–  Proxy for economic importance
–  We use three year window to count citations
•  Originality:
–  One minus Herfindahl of classes of patents
cited by patent.
•  Generality:
–  One minus Herfindahl of classes of patents
citing patent.
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Three-year citation counts for
portfolio firm patents
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Need for benchmark
•  Citation rates change over time:
–  Changing importance.
–  Changing technology mixture.
–  Changing propensity to cite overall.
•  Use all patents in same USPTO technology
class and grant year as control group:
–  Compute baseline citation rates for these matching
patents.
•  Similar issues for originality, generality.
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Average three-year citation counts
for matching patents
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Citations through 3rd Year after
Patent Grant
For Patents Applied for in Years Relative to
Buyout….
Citations
-3 to 0
+1 to +5
P-Value
Unadjusted 1.99
2.49
[0.000]
Relative
0.74
[0.000]
0.24
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Multivariate statistical analysis
•  We use Poisson and Negative Binomial
specifications for citations.
•  Consider both unadjusted and relative
citation counts.
•  Use individual year dummies as well as
combined “Post” dummy.
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Changes in citation count around
buyout transactions
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Fixed- and random-effects
specifications
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Three-year citation counts for
portfolio firms (with fixed effects)
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Other patent quality measures
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Amount of patenting
•  Truncation challenges:
–  As-yet unissued patents.
–  Assignment to corporate parents.
•  Responses:
–  Year and firm fixed effects.
–  Limiting to observations before 1999.
–  Limiting to firms with “early” and “late”
patents.
•  Still, this analysis is less conclusive
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Number of patents granted
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Composition of patent portfolios
•  Now looking at firm level:
–  Does the distribution of areas in which firms
pursue innovation change?
–  Where is increase in citations taking place?
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Comparing citations in well and
poorly populated classes
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Comparing citations in growing and
shrinking classes
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Changes in citation count,
controlling for patent class share
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Summary of empirical evidence
•  Patent quality appears to improve
following buyouts:
–  Not sacrificing originality or generality.
•  Amount of patenting is probably not
affected:
–  More challenging analysis, due to data
limitations.
•  Composition of patent portfolio appears to
focus on the more central technologies.
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Concern #1
•  We may be double counting secondary
buyouts:
–  Same patents may appear both before and
after a transaction.
•  Repeat analysis, treating these patents
separately:
–  We count only first transaction.
–  We delete these patents entirely.
–  Makes little difference.
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Concern #2
•  Are three years sufficient to capture
patents’ citation counts?
•  Citations are strongly serially correlated:
–  Three-year citation count is a good proxy for
total citation count.
•  We also repeat analysis using 2 and 4
years windows:
–  Find little or no difference to results.
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Concern #3
•  Are differences due to investors “cherry
picking” in divisional buyouts?
–  Parent company may keep the best patents.
•  We repeat analysis excluding divisional
deals:
–  Magnitude of difference in citations increases,
still significant.
–  Other results unchanged.
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Concern #4
•  Results may be due to investors selecting
targets with promising innovation
portfolios:
–  However, most targets are “old economy”
firms where innovation is relatively small part
of business potential.
–  Pattern shows most of the improvement in
years 2-3 following the transactions.
–  Selection of targets unlikely to introduce large
distortions.
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Wrapping Up
•  Innovation present a natural testing ground for
understanding motivation of private equity firms.
•  Our results are consistent with the positive view
of private equity:
–  We find an increase in innovation quality.
–  No evidence of decline in fundamental nature of
research.
–  See a focusing of patent portfolios.
–  Focusing of awards on high-impact areas.
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