IFS
Real Options, Patents, Productivity
and Market Value
November 2002
Nicholas Bloom (Institute for Fiscal Studies)
John van Reenen (Institute for Fiscal Studies & UCL)
Summary Part 1:
Patents Data
IFS
• 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
Summary Part 2:
Real Options
IFS
• 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
Previous Patenting Work
IFS
• 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.
Patents Data
IFS
• 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
IFS
Patents Data
frequency of patents by year
no. patents from that year
3000
2000
1000
0
1960
1967 1970
1980
application year
application year of the patent
1990
1994
1996
IFS
Patents Data
The distribution of firms by total patents: 1968-96
Firms
>1
>10
>25
>100
>250
>1000
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
IFS
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
Histogram of number of cites per patent
.3
frequency
.2
.1
0
012345
10
20
30
tot
no. cites
40
50
IFS
Citations Data
The Five Most Cited Patents
Patent Topic
Shell
Grand
Metropolitan
ICI
Synthetic Resins
Microwave heating
package
Herbicide
compositions
Unilever
Anticalculus
composition
British Oxygen Pharmaceutical
Corp.
Treatment
Grant
Year
1972
Cites
1976-96
221
1980
174
1977
130
1977
97
1975
89
IFS
Citations Data
• But the lag between patenting and citing can lead to
truncation biases when using citation weights
Lag from patenting to citation
citing frequency
.1
.05
0
0 1 2 3 4 5
10
20
lag
lag in years
35
IFS
Citations Data
• We correct for these truncation biases in citations
data using a Fourier series estimator
Actual and Normalizing Mean Total Cites Per Patent
15
10
5
0
1960
1980
application year
2000
IFS
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
Patenting & Productivity
IFS
• Standard production models (see Griliches, 1990)
usually assume Cobb-Douglas production
y  AGa K bLc
where: G is knowledge stock,
K is capital, and L is labour
• We proxy he knowledge stock using the stock of
patents (PAT) built up using the perpetual inventory
method.
• This allows us to estimate “ a ” – the return to patents
ln( y)  ln( A)  a ln( PAT )  b ln( K )  c ln( L)
• Using patent citations allow us to fine tune our
knowledge stock measure
IFS
Productivity Equation Results
Sales
All Firms
Patenters
Capital
0.333 * 0.436 * 0.438 * 0.468 *
Employment 0.650 * 0.558 * 0.554 * 0.502 *
Patent Stock
0.024 *
Citation Stock
0.030 *
No. Firms
2063
211
211
189
No. Obs.
18,068 2219
2219
1896
0.468 *
0.502 *
-0.012
0.039*
189
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.
Patenting and Market Value
IFS
• 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.
V
PAT
log( )  a (
)
K
K
where
V
Tobin' s Q  log( )
K
IFS
Market Value Results
Patent
Stock/Capital
Citation
Stock/Capital
No. Firms
No. Obs.
Log Tobin’s Q (log(V/K))
1.620*
-0.352*
205
2053
0.427*
0.491 *
182
1748
182
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.
Patents and Real Options
IFS
• 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).
Modeling Patents & Real Options
IFS
• 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
Ge G
l ( )  a
G Ge
where
l ( )
0

• Higher uncertainty leads to a lower elasticity of
embodiment – a slower pass through of patents into
production
Modeling Patents & Real Options
IFS
• 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.
Our Uncertainty Measure
IFS
• 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).
IFS
Mean Daily Returns Standard Deviation (%)
Our Uncertainty Measure
2.5
2
1.5
1
1970
1980
Year
1990
Notes: This is the unweighted mean of our measure of the standard deviation of daily returns over the year.
Mean Daily Share Returns – our entire sample
Patent Real Options Results
IFS
Tobin’s Q
Real Sales
Capital
0.451*
0.446*
Employment
0.517 *
0.553*
Patent Stock
0.025*
0.038*
Uncertainty
-0.036*
Uncertainty
Tobin’s Q
Uncertainty
 Pat. Stock
-0.015*
0.297*
-0.010*
 Tobin’s Q
0.913*
1.743*
-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.
Conclusion
IFS
• 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.