Private Equity and Long Run Investments: The Case of

Innovation

Josh Lerner, Morten Sorensen, and Per Stromberg

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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 (quarter-

to-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 “public-

to-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

0

10

20

3040

50

60

7080

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

InvestmentsExits

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Patents and LBOs in sample

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Patent Applications and Grants

0

200

400

600

800

1000

1983 1986 1989 1992 1995 1998 2001 2004 2007

ApplicationsGrants

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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 0.24 0.74 [0.000]

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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.