IFSReal Options, Patents, Productivity

and Market Value

November 2002

Nicholas Bloom (Institute for Fiscal Studies)

John van Reenen (Institute for Fiscal Studies & UCL)

IFSSummary Part 1:Patents 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

IFSSummary Part 2:Real 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

IFSPrevious 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.

IFSPatents 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

IFSPatents Data

frequency of patents by year

no. pate

nts

fro

m that ye

ar

application year of the patentapplication year

1960 1967 1970 1980 1990 19941996

0

1000

2000

3000

IFSPatents Data

>1 >10 >25 >100 >250 >1000

Firms 236 161 117 75 41 12

The Top 8 UK Patenting FirmsICI 8422

Shell 7200

SmithKline Beecham 3672

BP 3632

BTR 3432

Lucas Industries 3119

GEC 3054

Hanson 2892

The distribution of firms by total patents: 1968-96

IFSCitations Data

• Citations provide a proxy of patent values, which appear to be extremely variable.

• This allows us to fine tune our raw patent countsHistogram of number of cites per patent

fre

qu

en

cy

no. citestot

0 1 2 3 4 5 10 20 30 40 50

0

.1

.2

.3

IFSCitations Data

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

The Five Most Cited Patents

IFSCitations Data

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

Lag from patenting to citation

citin

g f

req

ue

ncy

lag in yearslag

0 1 2 3 4 5 10 20 35

0

.05

.1

IFSCitations Data

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

Actual and Normalizing Mean Total Cites Per Patent

application year1960 1980 2000

0

5

10

15

IFSThe 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

IFSPatenting & 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

cLbKaAGy where: G is knowledge stock,K is capital, and L is labour

)ln()ln()ln()ln()ln( LcKbPATaAy a

IFSProductivity Equation Results

Sales

All Firms 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 1896Notes: A full set of firm and time dummies is included.All coefficient marked * are significant at the 1% levelAll variables are in logs. Estimation covers 1968-1993.

IFSPatenting 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.

)()log(K

PATa

KV where )log('

KV

QsTobin

IFSMarket Value Results

Log Tobin’s 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% levelAll variables are in logs. Estimation covers 1968-1993.

IFSPatents 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).

IFSModeling 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

0)(

)(

l

whereGeG

GGe

al

IFSModeling 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.

IFSOur 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).

IFSOur Uncertainty Measure

Me

an

Da

ily R

etu

rns S

tan

da

rd D

evia

tio

n (

%)

Year1970 1980 1990

1

1.5

2

2.5

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

IFSPatent Real Options Results

Real Sales Tobin’s 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*

Tobin’s Q 0.913* 1.743*

Uncertainty Tobin’s 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% levelAll variables are in logs. Estimation covers 1968-1993.

IFSConclusion

• 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.