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Are New ‘Silicon Valleys’ Emerging? The Distribution of Superstar Patents across

US StatesCarolina Castaldi* and Bart Los**

*ECIS, School of Innovation Sciences, Eindhoven University of Technology

** Groningen Growth and Development Center (GGDC), University of Groningen,

DIMETIC Summerschool, Pécs, Hungary7 July 2010

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Outline of Research Project

• Do “Liberal Market Economies” have a comparative advantage in producing important inventions, in comparison to “Coordinated Market Economies”? (Hall & Soskice, 2001)

• Citation data from US Patent and Trademark Office not suitable for international comparisons.

• Overall objectives of the current project:– To gain knowledge about the relative

technology-specific ability of US States to generate ‘superstar’ patents

– To detect trends in spatial patterns of superstar invention over time

Superstars

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• Power Law/Pareto distribution: income distribution• Alternative: Lognormal distribution• Many other phenomena display similar statistical regularities:

• Size distributions of cities (Eeckhout, Levy, AER 2009) • Size distributions of files on the WWW (Mitzenmacher, 2004) • Distributions of citations to patents (indicator of importance of the underlying invention) are also known to have heavy tails (Silverberg & Verspagen, JEctrics, 2007)

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Stylized fact: Fat tails

• Curved part: lognormally distributed

• Linear part: Pareto distributed

• Drees-Kaufmann-Lux procedure to estimate cut-off point (Silverberg & Verspagen, 2007)

• Some inventions act as “focusing devices” (Rosenberg 1969) or initiate new paradigms (Dosi, 1982); see Sanditov (2006)

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Number of citations

Rig

ht c

umul

ativ

e fr

eque

ncy

Pareto plot

1975 Biotech

1975 Heating

Cutoff Biotech= 17 citations (bs mean 22), Heating=33 citations (bs

mean 32.3)

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Data

• NBER Patent-Citations Datafile– Book by Jaffe and Trajtenberg ( MIT Press,

2001)– Update of database by Bronwyn Hall (2006)– 2009 update cannot be used, since geographic

data on invention is missing

• Numbers of citations (1975-2002) to all utility patents granted by USPTO in 1963-2002• Our subset: 1975-2000 (application year)• Only patents granted to a US-based first

inventor• Classification of patents in 31 of the 36

technological fields used in Hall et al. (2002)

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Comparing citations received by patents: problems

• Point of departure: patents that receive more citations in subsequent patents have more value• Problem 1: Patenting behavior

varies across technology categories• Problem 2: Citations are not received

immediately• Problem 3: Citation behavior varies

over time

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Comparing citations received by patents: solutions

• Top patents determined by constructing citation-based rankings by category and application year for all patents issued;

• A first measure: top quantile (Hall & Trajtenberg, 2005; Akkermans, Castaldi & Los, 2009, Research Policy)

• An data-driven measure: Distinction between superstar patents and regular patents based on stylized fact that tail of size distribution is Pareto

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Application of tail estimation routine

• DK routine (based on Hill-estimator) applied for every category and year: two parameters estimated:– Cut-off point: nr superstar patents = patents

with citations larger than cut-off point– Alpha: “fatness” of the Pareto tail

• Confidence intervals for estimated counts obtained via bootstrap (Castaldi & Los, 2008, working paper)

• The overall analysis revealed two problems

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Number of citations

Rig

ht c

umul

ativ

e fr

eque

ncy

Pareto plot

1975 Biotech

1975 Heating

by Category

Problem 1: High variability

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Problem 2: Truncation

Different citation lags for superstar vs regular patents (e.g. cited half-life for 1980 patents in “information storage”: 7 years for regular patents; 12 years for superstar patents) => not very timely indicator

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Our proposal for a more timely indicator

• A probabilistic approach: developing a model which predicts the likelihood of a patent to become superstar based on a limited set of years

• Logistic regressions predicting probability pak,i for patent i with– a=age (citation window, at least 5 years) – category k

• Regressors: category- and age-specific variables that might predict eventual ‘superstarness’ at early ages

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Probabilistic approach

ncit = number of citations received (ln(ncit+1))frec = fraction of citations in most recent half of

existence;GEN=measure of generality;

Regressions were done for patents applied for in period 1975-1979. – Age/citation window from a=5 to a=20– To control for high variability of DK estimates, we use

the bootstrap mean to single out superstar patents– Estimates used to assess the probabilities of eventual

superstarness for more recent patents (1980-1995)– Why not predictions for 1996-2002? a=5, and many

patents applied for in 2001/2002 are not in database because they had not been granted yet.

iiakiakiakakiak

iak GENfrecncitp

p

3,2,1,,

,

1ln

Standardized by year

Regression Results

Category k=9 information storage(bold numbers: significantly different from 0 at 5%)

CONST CIT FREC GEN R2

k=9 a=5 -6.965 2.084 0.792 0.124 0.319a=1

0-

11.339 4.520 1.119 0.514 0.579a=1

5-

16.899 7.810 0.557 0.464 0.741

a=20

-100.14

750.30

5 -0.621 0.084 0.942

Average patent:

odds are 1:1060 that

it will be superstar

Average patent:

odds are 1:84000

that it will be

superstar

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Truncation problem solved…

Technologies: Emergence and Demise

• Ratio of 3-year moving averages of numbers of superstar patents between 1994 and 1976 < 0.7: – Agriculture, food and textiles (0.59); Heating; Organic

compounds; Apparel and textiles; Motors, engines and parts.

• Ratio of 3-year moving averages of numbers of superstar patents between 1994 and 1976 > 3.0– Drugs; Semiconductor devices; Surgery and medical

instruments; Computer peripherals; Computer hardware and software; Biotechnology (12.56, from 16.67 to 209.37)

• Ratios of shares of superstars in all patents (1994-1976): – Agriculture etc. (0.62); Heating (0.93); Drugs (0.77);

Semiconductor devices (0.81) Biotechnology (1.11, from 8.6% to 9.5%)

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Shares of Superstars in Total(selected technologies)

171976 1994

The Geographic Aspect

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Concentration indicators over states (all technology classes). 50 States + Washington DC + Puerto Rico

Superstar Generators(blue: 1976, red: 1994)

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ID

VTNH

Numbers of superstars scaled by population (in mlns.)

States: Emergence and Demise

• Ratio of 3-year moving averages of numbers of superstar patents between 1994 and 1976 < 0.8:– West Virginia (0.39); Oklahoma (0.67); Delaware (0.74)

• Ratio of 3-year moving averages of numbers of superstar patents between 1994 and 1976 > 4.0– Idaho (24.9); Vermont (4.70); Oregon (4.36); Georgia

(4.08)

• Ratios of shares of superstar patents in all patents, between 1994 and 1976: – West Virginia (0.43); Oklahoma (0.76); Delaware (0.66);

Idaho (4.44); Vermont (1.28); Oregon (1.64); Georgia (1.67)

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New Silicon Valleys?

• No systematic summary yet, though:– Idaho: no superstar patents in semiconductors in

1975-1984, on average 15 per year in 1993-1995;– Vermont: mainly small state effect;– Oregon: very good performance in computer

hardware and software, less than 1 superstar patent per year in the first 11 years, almost 9 on average in 1993-1995;

– Georgia: solid superstar patenting performance in several technologies, i.e. Biotechnology, communications and computer hardware and software

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Conclusions• New operationalization of top inventions:

• Tail estimators allow endogenous determination • More timely indicator thanks to probabilistic

method• Relative size of the tail differs across fields• Results track the emergence of ‘new

technologies’ => we can use patent data to identify emerging technology fields and link them

• US States also emerge and decline with regard to technological leadership. The trends are clearer when superstar patents are considered.

• Reality check: link the identified superstar patents to case studies