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Economics of Innovation

Jaffe’s model

Manuel Trajtenberg2005

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Technological Opportunity and Spillovers of R&D: Evidence from

Firms’ Patents, Profits, and Market Value

byAdam Jaffe

AER, Vol. 76 (5), Dec 1986, 984-1001

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Introduction

.

The main goal is to assess the effect of key “supply side” factors,

(1) spillovers from other firms, and

(2) “technological opportunity,” and the corresponding “tech position” of firms

on the “productivity” of own R&D, and profits

Key issue: how to define these concepts, how to actually measure them?

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The key concepts

Spillovers:

• presumed to have a positive technological effect,

• but a potentially negative economic effect through competition.

“Tech opportunity”: “exogenous variations in costs and difficulty of innovation in different areas.”

“Tech position” of firms vis tech opportunities: endogenous, but slowly moving

Concepts not directly observable – use patent data to measure them.

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Front page of a patent (partial)Frohman-Bentchkowsky, et. al. May 13, 1980

Electrically programmable and erasable MOS floating gate memory device employing tunneling and method of fabricating same

Inventors: Frohman-Bentchkowsky; Dov (Haifa, IL); Mar; Jerry (Sunnyvale, CA); Perlegos; George (Cupertino, CA); Johnson; William S. (Palo Alto, CA).

Assignee: Intel Corporation (Santa Clara, CA).

Current U.S. Cl.: 365/185.29; 257/321; 326/37; 327/427; Field of Search: 365/185, 189; 307/238; 357/41, 45, 304 References Cited 3,500,142 Mar., 1970 Kahng 365/1854,051,464 Sept., 1977 Huang 365/185

Primary Examiner: Fears; Terrell W. 16 Claims, 14 Drawing Figures

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Patent Class Definitions: examplesCLASS 365, STATIC INFORMATION STORAGE AND

RETRIEVAL

CLASS DEFINITION: This is the generic class for apparatus or corresponding processes for the static storage and retrieval of information. For classification herein, the storage system must be (1) static, (2) a singular storage element or plural elements of the same type, (3) addressable.

CLASS 257, ACTIVE SOLID-STATE DEVICES E.G., TRANSISTORS, SOLID-STATE DIODES)

CLASS DEFINITION: This class provides for active solid-state electronic devices, …, usually semiconductors, which operate by the movement of charge carriers - electrons or holes - which undergo energy level changes within the material and can modify an input voltage to achieve rectification, amplification, or switching action, and are not classified elsewhere.

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The Technological Location of Firm i

Characterize each firm by the vector:

fij: the % of firm i patents in “patent category” j.

The USPTO patent classification system (purely tech based, not “industries”):

• ~ 400 patent classes (328 back in the 1980’s).

• ~ 150,000 patent sub-classes

Jaffe aggregated the 328 patent classes into 49 “patent categories”.

)...,( ,2,1 iKiii fffF

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Measuring spillovers

10 ,)')('(

' ij

jjii

jiij P

FFFF

FFP

A measure of technological proximity between firms: the “angular separation” (or uncentered correlation) of the vectors Fi and Fj:

The potential spillover pool:

R&Dj: the R&D expenditure of firm j

jij

iji DRPS )&(

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Data• All patents granted to 1700 manufacturing firms, for 1969-79: 260,000 patents

• Firms linked to Compustat

• Two tech “positions:” one based on patents up to 1972, the second based on patents after 1972.

• Thus each firm: two 49 elements vector, one for each period.

1969 19791972

Fi1 Fi2

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http://www.compustat.com/www/

Compustat Data Packages Compustat Offerings>Compustat® Data>Compustat North America>Compustat Global>Compustat Xpressfeed>Compustat Xpressfeed Loader>Compustat Historical>Compustat Unrestated Quarterly>Research Insight on the Web>Research Insight>Market Insight>Standard & Poor's Custom Business Unit

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Establishing the tech position of firms

• Use clustering algorithm: identify firms with a similar tech focus, so that they face the same state of technological opportunity; based again on the vectors Fi ’s.

• Found 21 clusters; done twice, pre- and post 1972.

• About 1/3 of firms change clusters between the 2 periods.

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Cluster# Firms

19721978

Adhesives & Coatings3024

Chemistry, Carbon4539

Chemistry, Electrochemistry

1613

Chemistry, Organic2122

Cleaning and Abrading1614

Compositions2020

Cutting2415

Elec. Computers & Data Proc.2120

Elec. Transmission & Systems3426

Electronic Communication2832

Fluid Handling2724

Cluster# Firms

‘72‘78

Food3429

Measuring & Testing3120

Medical1325

Metals and Metal Working3329

Misc. Consumer Goods2429

Power Plants (Non electric)5129

Receptacles & Packages2436

Refrigeration & Heat Exch.2937

Static Structures2738

Vehicles2536

All Firms573557

Technological clusters

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The patenting equationPatents as a function of:

• own R&D - flow

• Spillovers Pool (R&D of others, weighted by their “tech proximity):

• Interaction between the two (“absorptive capacity”)

• Dummies for Tech clusters

n

ij jiji DRPS )&(

i

iiiii

dummiestech

SDRSDRPat

)&()&( 3210

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The Profits EquationHow to measure profits? Operating income before depreciation

Profits as a function of:

• own R&D stock

• Spillovers Pool

• Interaction between the two

• Dummies for Tech clusters

• Capital

• Market share

• Market concentration – C4

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The Tobin’s q equation

Tobin’s q: Market value/Capital

As a function of:

• R&D/Capital

• (R&D/Capital) x Spillovers Pool

• Dummies for Tech clusters

• Market share

• Market concentration – C4

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The estimating equations

Log of:

PatentsProfitsTobin’s q

Log(R&D)x flowx stock

Log(S-pool)xx

log(R&D)xlog(S-pool)xx

R&D/Capitalx

[R&D/Capital]xlog(S-pool)x

Log(Capital)x

Tech cluster dummiesxxx

Market Share, C4xx

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Statistics for Regression Variables

Levels (846 Obs)

MeanMedian

Patents35.59.3

Gross Profit207.932.7

Tobin’s q1.020.84

Annual R&D25.73.41

R&D Stock12112.5

Capital Stock968153

Annual Spillover Pool2,6222,438

Spillover Pool Stock10,0429,986

Market Share5.443.24

Four-Firm Concentration38.337.1

Note : All nominal variables are millions of 1972 dollars. Market share and concentration are percentages.

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Data issues• 432 firms, 1/3 of those that report R&D, but they

account for 95% of total R&D (possible selectivity bias “against” small firms).

• Two cross-sections, centered on 1973 and 1979. Each cross section: average of 3 years data,

“smoothing”.

• R&D stocks (for the profits and market value equations): computed assuming 15% depreciation of R&D; extrapolation into the past using average growth rate.

• Market shares only for 1972 – proxies for 1979.

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Econometric issues

• Endogeneity of R&D, Capital, market share: shock (e.g. unobserved “management skill”) may both lead to higher patents, profits, etc. and to higher investments and market share.

• Measurement error in some of the X’s, e.g. R&D (because assume only contemporaneous effect for patents, the way the stock is constructed, etc.).

iii

iiiii

dummiestechCMarkShCap

SDStockRSStockDRProfits

)4(

)&() &(

654

3210

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Estimation• First estimates OLS, but as said possible endogeneity.

• Estimate first differences between two cross sections (like fixed effects); but just 2 cross sections, very imprecise, may exacerbate errors in variables problem

• Bring in instruments, estimate 3SLS: like 2SLS, but also system of equations to take into account possible correlations of error terms across equations. White s.e.

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Instruments

Need IVs for R&D, Capital and Market Share. Use Industry variables (given for the individual firm). Each firm belongs to a bunch of SIC, according to its sales, so take the weighted average – that gives variation across firms even if similar.

• Industry R&D, Sales, growth rate

• Industry MSE - minimum efficient scale (IV for Capital)

• Spillover pool (and interactions between it and the industry variables)

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Table 5: 3SLS Estimates - 432 obs(elasticities)

Log of:

PatentsProfitsTobin’s q

Log(R&D)0.88 (0.18)0.18 (0.04)

Log(S)0.51 (0.10)-0.09 (0.05)-0.058 (0.03)

log(R&D)xlog(S)0.35 (0.05)0.06 (0.02)

R&D/Capital3.31 (0.21)

[R&D/Capital]xlog(S)0.80 (0.10)

Log(Capital)0.82 (0.40)

Log(C4)-0.22 (0.04)-0.53 (0.08)Log(72 Market share) 730.19 (0.06)0.31 (0.05)

S: spillover pool (zero mean)

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Log of:

PatentsProfitsTobin’s q

on Technological Cluster Effects :

(1973)81.388.498.6

(1979)94.476.590.2

s.e. (1973)0.8423.620.652

s.e. (1979)0.9123.510.420

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Table 5 – cont.

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Results for the patents equation

• Elasticity of patents w.r.t. R&D: 0.88 for average firm, higher for those with above-average

spillover pool.

• Elasticity w.r.t. to S-pool: 0.51 + 0.35 log(R&D); for those with mean R&D (1.8): 1.1 – very large!

• Thus, if everybody increases R&D by 10%, total patents increase by ~ 20% (0.088+0.035x(1.8x1.1)+0.051).

• “Return” of 2 patents per million $ own R&D, 0.6 patents per 10 M$ of others R&D.

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Results for the profits equation

Compute gross rate of return to R&D (or to capital); start with estimated elasticity of R&D:

DRDR

DR

DR &&

&

&

Gross rate of return to R&D:

(mean /mean R&D stock) = 208/121= 1.7

Estimated elasticity x 1.7 = 0.18 x 1.7 = 0.31 (in Jaffe it is 28%, difference apparently because of mean of the ratio, not ratio of the means)

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Profit equation – cont.

Gross rate of return to capital:

Mean profits/mean capital = 208/968 = 0.21

times elasticity of capital: 0.21 x 0.825= 0.18%(in Jaffe it is 15%)

Thus, returns to R&D (0.31) almost 2 times larger than return to conventional capital (but much higher depreciation).

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Profit equation – cont. 2

Spillover pool: negative direct effect (-0.095, only marginally significant), but positive through interaction (+0.058). Net effect for average log(R&D):

-0.095 + 0.058 (3.09) = +0.084

For firms with little R&D (below half s.d. of mean log R&D): negative net effect

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Tobin’s q equation

• Similar to profit equation: negative direct effect of S, positive interaction – firms doing lots of R&D benefit from spillovers pool.

• An R&D dollar increases market value 3 times as much as a dollar invested in physical capital (see coefficient of R&D/Capital).

• C(4) decreases market value, own market share increases q.

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Pooled OLS Estimates for 1973 and 1979 (846 Obs)

Log of:

PatentsProfitsTobin’s q

Log(R&D)0.73 (0.15)0.20 (0.05)

Log(S)0.63 (0.11)-0.08 (0.04)-0.08 (0.05)

log(R&D)xlog(S)0.17 (0.03)0.03 (0.02)

R&D/Capital2.95 (1.52)

[R&D/Capital]xlog(S)0.53 (0.19)

Log(Capital)0.56 (0.02)

Market Share, C4-0.16 (0.04)-0.44 (0.05)

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Log of:

PatentsProfitsTobin’s q

F-Stats for Tech Cluster Dummies (20 D.F) :

19734.25.85.6

19793.95.75.5

0.940.440.77

s.e.0.8063.460.509

2R

Lower coefficients for R&D in OLS: because endogeneity expected positive bias, but errors in measurement the other way.

OLS – cont.

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Technological clusters & tech opportunity

High correlation over time of the tech dummy coefficients from the patents equation, low corr. for the profits or market value:

Technological persistence, but high profits in clusters in 1973 get competed away by 1979. Indeed,

• positive corr. between net entry into clusters, and size of coefficients in 1973;

• negative corr. between change in dummies in Tobin’s q equation, and entry (i.e. entry lowers market value).

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Key contributions

1. Method to measure spillovers, tech position and tech proximity (used since).

2. Findings:

• Large impact of spillovers in 3 equations: positive for patents, negative (direct) for profits and market value, but positive if do at least average R&D.

• Importance of “absorptive capacity.”

• Large (private) returns from R&D (twice as large as for physical investment).