1 Are New ‘Silicon Valleys’ Emerging? The Distribution of Superstar Patents across US States Carolina 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, Hungary 7 July 2010

2 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 3 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)

4 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) Cutoff Biotech= 17 citations (bs mean 22), Heating=33 citations (bs mean 32.3)

5 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 ( ) to all utility patents granted by USPTO in Our subset: (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)

6 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

7 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

8 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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by Category Problem 1: High variability

11 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

12 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 p ak,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

13 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 –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 ( ) –Why not predictions for ? a=5, and many patents applied for in 2001/2002 are not in database because they had not been granted yet. Standardized by year

Regression Results Category k=9 information storage (bold numbers: significantly different from 0 at 5%) CONSTCITFRECGENR2R2 k=9a= a= a= a= Average patent: odds are 1:1060 that it will be superstar Average patent: odds are 1:84000 that it will be superstar

15 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 to ) Ratios of shares of superstars in all patents ( ): –Agriculture etc. (0.62); Heating (0.93); Drugs (0.77); Semiconductor devices (0.81) Biotechnology (1.11, from 8.6% to 9.5%) 16

Shares of Superstars in Total (selected technologies)

The Geographic Aspect 18 Concentration indicators over states (all technology classes). 50 States + Washington DC + Puerto Rico

Superstar Generators (blue: 1976, red: 1994) 19 ID VT NH 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) 20

New Silicon Valleys? No systematic summary yet, though: –Idaho: no superstar patents in semiconductors in , on average 15 per year in ; –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 ; –Georgia: solid superstar patenting performance in several technologies, i.e. Biotechnology, communications and computer hardware and software 21

22 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