Real%20Options,%20Patents,%20Productivity%20and%20Market%20Value%20November%202002

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Real Options, Patents, Productivity and Market
ValueNovember 2002

• Nicholas Bloom (Institute for Fiscal Studies)
• John van Reenen (Institute for Fiscal Studies
  UCL)

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Summary Part 1Patents Data

• There is a consensus that technological advance
  is crucial in the new economy
• Patents provide a powerful indicator of this
  technology
• We hand-match patents from over 12,000 assignees
  to 450 UK parent firms.
• Using this dataset we show a strong and
  significant effect of patents on
• Productivity
• Market Value
• Patent citations are also shown to
  informative

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Summary Part 2Real Options

• We use this data to test new Real Options
  theories
• Embodying new technology requires heavy
  investment, training and marketing.
• When firms patent technologies they have the
  option to see how market conditions develop
• This generates patenting real options
• Hence, higher uncertainty will lead to a more
  gradual technology take up
• This turns out to be empirically significant

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Previous Patenting Work

• Toivanen, Stoneman and Bosworth (1998) and
  Bosworth, Wharton and Greenhalgh (2000) find
  patenting effects on market value in UK firms.
• Griliches (1981), Hall (1993), and Hall, Jaffe
  and Tratjenberg (2001) report effects on market
  value in US firms.
• Greenhalgh, Longland and Bosworth (2000) report a
  positive employment effect of patenting in UK
  firms.

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Patents Data

• We constructed the new IFS-Leverhulme dataset
  using patenting, accounting and financial data.
• The patenting data was hand matched from the
  12,000 largest US PTO patenting assignees to
  their UK parent companies.
• The remaining 128,000 patenting subsidiaries were
  then computer matched which is less accurate.
• This provides reliable firm level patenting
  information from 1968 to 1993 on the UK and
  Overseas subsidiaries of about 200 UK firms

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Patents Data
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Patents Data
The distribution of firms by total patents
1968-96
gt1 gt10 gt25 gt100 gt250 gt1000
Firms 236 161 117 75 41 12
The Top 8 UK Patenting Firms
ICI 8422
Shell 7200
SmithKline Beecham 3672
BP 3632
BTR 3432
Lucas Industries 3119
GEC 3054
Hanson 2892
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Citations Data

• Citations provide a proxy of patent values, which
  appear to be extremely variable.
• This allows us to fine tune our raw patent counts

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Citations Data
The Five Most Cited Patents
Patent Topic Grant Year Cites 1976-96
Shell Synthetic Resins 1972 221
Grand Metropolitan Microwave heating package 1980 174
ICI Herbicide compositions 1977 130
Unilever Anticalculus composition 1977 97
British Oxygen Corp. Pharmaceutical Treatment 1975 89
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Citations Data

• But the lag between patenting and citing can lead
  to truncation biases when using citation weights

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Citations Data

• We correct for these truncation biases in
  citations data using a Fourier series estimator

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The IFS-Leverhulme Dataset

• We match patents with Datastream accounting data

Median Mean Min. Max.
Capital (1985 m) 143 744 1.6 18,514
Employment (1000s) 8,398 24,374 40 312,000
Sales (1985 m) 362 1,224 1.15 20,980
Market Value (1985 m) 153 740 0.29 19,468
Patents 3 12.6 0 409
Patent Stock 10 42.6 0 1218
Cite Stock 49.2 202 0 5157
Uncertainty 1.39 1.47 0.60 6.6
Observations Per Firm 22 20 3 29
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Patenting Productivity

• Standard production models (see Griliches, 1990)
  usually assume Cobb-Douglas production
• We proxy he knowledge stock using the stock of
  patents (PAT) built up using the perpetual
  inventory method.
• This allows us to estimate the return
  to patents
• Using patent citations allow us to fine tune our
  knowledge stock measure

where G is knowledge stock, K is capital, and L
is labour
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Productivity Equation Results
Sales Sales Sales Sales Sales
All Firms Patenters Patenters Patenters Patenters
Capital 0.333 0.436 0.438 0.468 0.468
Employment 0.650 0.558 0.554 0.502 0.502
Patent Stock 0.024 -0.012
Citation Stock 0.030 0.039
No. Firms 2063 211 211 189 189
No. Obs. 18,068 2219 2219 1896 1896
Notes A full set of firm and time dummies is
included. All coefficient marked are
significant at the 1 level All variables are in
logs. Estimation covers 1968-1993.
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Patenting and Market Value

• The effect of patents on firm performance can
  also be measured using forward looking market
  values
• Following Griliches (1981), Bosworth, Wharton and
  Greenhalgh(2000), and Hall et al (2000) we use a
  Tobin's Q functional form.

where
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Market Value Results
Log Tobins Q (log(V/K)) Log Tobins Q (log(V/K)) Log Tobins Q (log(V/K))
Patent Stock/Capital 1.620 -0.352
Citation Stock/Capital 0.427 0.491
No. Firms 205 182 182
No. Obs. 2053 1748 1748
Notes A full set of firm and time dummies is
included. All coefficient marked are
significant at the 1 level All variables are in
logs. Estimation covers 1968-1993.
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Patents and Real Options

• Bertola (1988), Pindyck (1988), Dixit (1989) and
  Dixit and Pindyck (1994) first noted the
  importance of real options in generating
  investment thresholds for individual projects.
• Abel and Eberly (1996) and Bloom (2000) extend
  this theory to show how real options lead firms
  to be cautious in responding to demand shocks.
• This cautionary effect of real options on
  investment has been shown empirically by Guiso
  and Parigi (1999) and Bloom, Bond and Van Reenen
  (2001).

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Modeling Patents Real Options

• To model this caution effect of real options we
  define G as the firms potential knowledge stock
  and Ge as its embodied knowledge
• We can then define the elasticity of embodied to
  actual knowledge as
• Higher uncertainty leads to a lower elasticity of
  embodiment a slower pass through of patents
  into production

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Modeling Patents Real Options

• We prove that the effect of total patents (PAT)
  will be positive
• But the effect of new patents on productivity
  will be reduced by higher uncertainty - the
  caution effect
• The direct effects of uncertainty will be
  ambiguous.
• Interestingly, while this is true for
  productivity, market values are forward looking.
• To investigate these effects we add in
  uncertainty levels and interaction effects.

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Our Uncertainty Measure

• Our uncertainty measure is the average daily
  share returns variance of our firms over the
  period
• Using a firm specific time invariant uncertainty
  measure matches the underlying theory
• This share returns uncertainty measure has been
  used before by Leahy and Whited (1998) and Bloom,
  Bond and Van Reenen (2001).

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Our Uncertainty Measure
Mean Daily Share Returns our entire sample
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Patent Real Options Results
Real Sales Real Sales Tobins Q Tobins Q
Capital 0.451 0.446
Employment 0.517 0.553
Patent Stock 0.025 0.038
Uncertainty -0.036 0.297
Uncertainty Pat. Stock -0.015 -0.010
Tobins Q 0.913 1.743
Uncertainty Tobins Q -0.265 -0.073
Firm Dummies No Yes No Yes
No. Firms 211 211 205 205
No. Obs. 2053 2053 2037 2037
Notes All coefficient marked and are
significant at the 1 and 10 level All variables
are in logs. Estimation covers 1968-1993.
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Conclusion

• Patents appear to play an important role in
  determining productivity and market value
• But their impact on productivity is delayed when
  higher uncertainty reduces the rate of
  technological embodiment
• Hence, micro and macro stability could play a
  large role in encouraging technological
  development.