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

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,

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

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  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

  3. Superstars • 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 (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)

  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

  9. Problem 1: High variability by Category

  10. 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

  11. 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,ifor 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

  12. 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. Standardized by year

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

  14. Truncation problem solved…

  15. 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%)

  16. Shares of Superstars in Total(selected technologies) 1994 1976

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

  18. Superstar Generators(blue: 1976, red: 1994) ID VT NH Numbers of superstars scaled by population (in mlns.)

  19. 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 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

  21. 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

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