Olof Ejermo, olof.ejermo@circle.lu.se CIRCLE, Lund University

1. Methods and applications using innovation count data presented on Dec 14, 2012, at the NORSI/PING course on ‘Survey of Quantitative Research’ Olof Ejermo, olof.ejermo@circle.lu.se CIRCLE, Lund University

2. Outline • Presentation of count innovation data types • Econometrics of count data

3. ’Diffusion’ Spillovers Imitation Invention Innovation • commercialized • putintouse R&D Patents Frequentlyused innovation measures and theirrelationship to innovation stages Innovation counts Citations – and related Innovation expenditures

4. Commensurability (Smith, 2005) • Commensurabilityrefers to the ability to standardize a measure on a commonscale • Suchstandardization makes it possible to add up data to form an aggregatemeasuresuch as the amount of R&D undertaken by a country • Commensurability is oftenimplicitlyassumedbutcancertainly not be taken for granted. • Innovation indicators are especiallydifficult to rendercommensurablesince innovation has no standardized definition

5. Subject vs. objectapproaches • Subjectapproaches asks actors, usuallyfirms, throughsurveys • Indicatorsthereforereflect the activities of innovation subjects • Research & Development (R&D) could be said to belong to this class (Smith does not) • Wefind innovation expenditures and innovation introductionindicators (CIS) - • Objectapproachesfocus on the outcome of the innovation process and tend to capture visible products • Innovation countsbelonghere

6. Research & Development (R&D) • Research & Development (R&D) is the oldest and thereforemostcommon innovation indicator. • Advantagesinclude a standardizedprocedure for collectionacross OECD countries • Commensurability ’achieved’ by the use of the standardized input measuremoney • Disadvantagesincludeprimarily the assumptions that it is Research & Development that matters for innovation. This is less oftentruethan it used to be. • In addition, R&D can be of very different kinds: e.g. basic vs. applied, close/faraway from markets. Commensurability is therefore ’artificial’

7. Innovation surveys • Innovation surveys (e.g. CIS) asks firmaboutinnovation-relatedactivities. • Muchmoretargetedtowards innovation activity than R&D In CIS, • Innovation expenditureswhichmaycompriseincludetraining and marketing activitiessuch as advertising. Also, includes R&D. • Asks explicitlyiffirmsintroduceproducts/processes

8. Patents • Patents are technicaldocumentswhich grant the holder a monopoly on the technology • Idea: weneed to promote invention whichwouldotherwise not takeplace • In exchange for the monopoly, applicants must reveal the technologicalcontent in full • Requirements on I. novelty; II. inventive activity; III. industrial applicability. • Monopolyusually lasts 20 yrs (ifrenewalfees are paid) • Most important patent bureaus are USPTO, EPO, JPO

9. Patents as invention indicators • A morequalifiedindicator of inventive activity than R&D: ’somethingcameout’ • Note: R&D may not lead to patenting, nor doespatentingrequire R&D • Yet: high correlationbetween R&D and patents  patent countsmay be used as proxies for ’inventiveness’

10. Graph on rate of patenting vs. time Source: Hall (2005)

11. The patent ”explosion” • Rise in patentingdue to • Underlyingmore innovative activity in existingfields • New technology fieldsemerge, technologicalopportunities (biotech, ICT) • New fieldsbecomepatentable (biotech, ICT) • Changingstrategies of firms • Changingease of patenting, i.e. judicial system • All of the above Theseneed not contradicteachother

12. Patent citations • Applicantsrequired to reveal ”prior art” (esp. In the US) • Different from citations in science, patent citations delimit the scope of the patent • Thus, while a patent applicantwould like as broad a patent as possible (gives moremonopoly rights), citations serve the function of acknowledging the rightfultechnologicalproperty of others

13. Patent citations for innovation studyuses • citations to a patent indicatesusefulness, whichhelpstowardsqualifying it as an innovation indicator • citations indicatedirections of knowledgeflows - spillovers, connectedness of firms, people, regions and countries

14. Advantages of patent data • Highlydetailed information on: • date of invention (filing date of application, granting date) • high geographical precision -addresses of inventors (and applicants) cy, region, zipcode • information on technology • Long time-series • Highlyavailable

15. But be careful… • not all innovations are patentable • not all patentable innovations are patented • there are biases/differences in the propensity to patent depending on: • industry • firm size (but patents/R&D usuallyhigher for small) • invention type (e.g. product-lifecycle position) • whatcan be patented has evolved over time • the cost of imitation • technologicalopportunities

16. Also… • substantialshare of patenting is for strategic, preemptive, competition • secrecyoftenmoreefficientprotectionmechanism

17. Whatcanwedo with patents in innovation research? • Wecanlearnabout the dynamics of technologies • Studyingapplicants (usuallyfirms) wemay get insightintofirmstrategiesregarding IP and innovation outcome • Studyinginventorswemay get insightsintowhat makes peopleintoinventors

18. Not only patent counts Whenusing patent data considerotherindicators: • Renewal of patents, citations, claims, familysize, opposition • All of the abovecan be used to qualityadjust patents to addresscommensurability

19. What is the value of patents? Gambardella, A., Harhoff, D. & Verspagen, B. (2008), “The value of European patents,” European Management Review, 5, 69-84. • Picked patents with priority date 1993–1997 • Granted by the European Patent Office (EPO) • Survey to first inventor in Denmark, France, Germany, Hungary, Italy, the Netherlands, Spain or the UK. • Returned info. on value of > 9000 patents

20. Paper makes twocontributions 1. measurement and estimates of the economic value of patents 2. assessment of relationship between survey-based measure and indirect indicators commonly used in the literature: number of forward citations, backward references, claims, and countries in which the patent is applied for.

21. Whyimportant to knowabout the value of patents? • Wedon’tknow the economicsignificance of patentingdespiteitsextremely high level • Firmsmaybenefit from the possibility to estimate the value of theirintellectualproperty • Investors maymoreappropriately be able to estimatefirms’ value • Patents are used as a proxy for innovation performance. • But do not account for quality or heterogeneity across • patents. • The literature has then resorted to the use of forward citations, or other indicators, as proxies for innovation quality or economic value • Yet, the limited availability of direct measures of value implies that the relationships between these indicators and the value of patents are still largelyuntested.

22. The SurveyQuestion • ‘Suppose that on the day on which this patent was granted, the applicant had all the information about the value of the patent that is available today. In case a potential competitor of the applicant was interested in buying the patent, what would be the minimum price (in euro) the applicant should demand?’ • We offer a menu of 10 interval responses: less than €30K; 30–100K; 100–300K; 300K–1M; 1–3M; 3–10M; 10–30M; 30–100M; 100–300M; more than 300M.

23. Value of patents (according to inventors) < 1% Figure 1 Distribution of VALUE. The figure shows that the PatVal-EU patent VALUE distribution is skewed. Since the difference in the logs of the boundaries of the intervals is roughly constant, the distribution in the figure is an approximation of a log-normal. Even the log-normal distribution looks skewed.

24. Valuefindings • Valuecomprisesbothstrategicvalue and invention value • Authorsfind that estimates are reasonable, thoughsomewhathigherthanreported in literature • Use regression results to estimatevalue of all patents based on qualityindicators • Estimatedmeanvalue > 3 million Euro, median 400 thousand, mode value 6-7 thousand Euros • Highestreportedvalues in Chemicals, Pharmaceuticals

25. Patents as innovation indicators • Regressions of patent value on patent qualityindicators show that these are all highlysignificantacrossspecifications • Buttheyaddfairlylittleexplanatory power to overall patent value • Meansqualityindicators ”work” but not yetincrediblywell • Specificcases/technologies, succeedbetter: a) Tennis racket patents and citations in Dahlin, K. B. & Behrens, D. M. (2005), “When is an invention really radical?,” Research Policy, 34 (5), 717-737 and b)the pioneering Trajtenberg, M. (1990), “A Penny for Your Quotes: Patent Citations and the Value of Innovations,” The Rand Journal of Economics, 21, 172-187.

26. How far does the effects of new knowledgespread? (Jaffe et al. 1993) • Why is this an importanttopic? Knowledge has public goodproperties: it can be used by otherswithoutdiminishingits original role. Thus, public investments in knowledge has positive externalities (spillover) effects. • Jaffe et al asks how far thesespillovereffectsreach. • How? By examining the geographicalreach of patent citations.

27. Jaffe et al: looking at the merereach of citations could be misleading. Why? If an industry has all its patents in one region, citations willmerelyreflect the pre-existingconcentration. • In other words, many semiconductor patents come from Silicon Valley, wemaythusobservelocalized citations eveniflocation provides no advantage • Ifwecontrol for the effect of the pre-existing pattern of geographic concentration of technologically related activities we get a cleaner test of how localized spillovers are

28. How? For patent A citing patent B find a controlpatent C in the same technology as A and from the same year. C doesnotcite B. • Calculate pc the rate at whichciting patents are from the same country/state/SMSA as the cited patent • Calculate the corresponding rate p0 for control patents • p0 is a baseline probability of co-localization for that technology with the cited patent • pc-p0 therefore reflects probability over and above what is normally localized for a certain technology • T-statistic tests if citing patents more localized than controls

29. Two samples of "originating" patents - 1975 patent applications (950 patents that had received a total of about 4750 citations by the end of 1989) - 1980 patent applications (1450 patents that had received about 5200 citations by the same time) • In each sample a) patents granted to U. S. universities b) two samples of U. S. corporate patents (Top corporate and Other corporate)chosen to match the university patents by grant date and technological distribution

31. Conclusions • Citations are highly localized even after comparing with controls, as seen from t-statistic on highly local level (SMSA) • There are few differences in localization between the citations of university and corporate patents. The largest difference is that corporate patents are more often self-cited, and self-cites are more often localized

32. Later research • Spilloverscould be the result of inventornetworks, e.g. former colleagues or universitystudy mates whose work you are familiar with  severalpapers on social networks that useinventor data from patents • Mobility on labor markets, partiallyinternalizingspillovers in markets, is nowconsidered an importantmechanism for knowledge diffusion • Seealsolectures by Andrea Morrison.

33. On-going research at CIRCLE on inventors • Database on 23,000 Swedish inventors linked to directories of individual data at Statistics Sweden (80% of all EPO, Swedish inventors) • Papers/WPs on • Demographics of inventors (Ejermo & Jung) • Mobility of inventorsacrossfirms (Ahlin & Ejermo) • Patenting in academia (Ejermo & Lavesson) • Business cycles and inventivestart-ups (Ejermo & Xiao)

34. Objectapproaches • For objectapproaches the outcome, the product is in focus. • Processes are rarelyaddressedwhichmeans that manufacturing is oftenbettercaptured Two important types: 1. Expert appraisals of product innovations. Main example the SPRU database, University of Sussex 2. Literature based innovation output data (LBIO). Collection of trade journal product announcements. Main example the US Small Business Administration (SBA) database

35. The SPRU database • Collected information on major technical innovations in British industry • Information on sources and types of innovation, industry innovation patterns, cross-industry linkages, regional aspects • Expert panel comprising 400 technical experts, drawn from a range of institutions, to identify major innovations • Innovations from all sectors of the economy, 1945-1983 • The database covers about 4,300 innovations.

36. Pavitttaxonomy: innovation patternsdiffer by sector • Pavitt, K. (1984), “Sectoral patterns of technical change: Towards a taxonomy and a theory,” Research Policy, 13, 343-373. • Tidd, J., Bessant, J. & Pavitt, K. (2005), Managing Innovation, Hoboken NJ, John Wiley • Technologicaltrajectories in sectors • Firms in different sectors follow different technological trajectories: prevailing modes of innovation • Examples: • Huge R&D laboratories, large-scale manufacturing plants vs. small firms • Product innovation vs. process innovation • In-house innovation activities vs. external partners • R&D lab vs. ”design office” or”systems department”

37. The Pavitt taxonomy – 5 major trajectories • Scale-intensive (e.g. cars, steel) • Science-based (e.g. electronics, chemistry, pharmaceuticals) • Specialized suppliers (e.g. instruments, software) • Supplier-dominated firms (e.g. agriculture, traditional manufacture) • Information-intensive (e.g. finance, retailing, publishing, travelling)* *added compared to the original taxonomy

38. Characteristics of innovation • Size of innovating firms – big in chemicals vehicles material, aircraft…, small in machinery, instr., software • Type of products – price sensitive in bulk goods, performance sensitive ethical drugs • Sources of innovation: suppliers in agriculture and traditional manufacture (e.g. textiles), customers in instrument machinery & software, in-house in chemicals & electronics…., basic research in ethical drugs • Locus of own innovation: R&D-labs in chemicals & electr., prod. eng. depts in automob. & bulk, design in machine building, systems depts in service industries

41. Discussion of indicator • Advantages: • expert opinion of what is an innovation • no no-response problem • addresses of innovators may be available  can be given geographical dimension • Disadvantages: • Ignores incremental innovations and process innovations  bias against service innov. • which experts?

42. LBIO – SBA • The SBA database shows innovations introduced to the market by small firms in the US in one year, 1982 • Used for a series of papers by Acs and Audretsch in the 1980s and 1990s • constructed through an examination of about one hundred trade, engineering, and technology journals

43. Examples of research using SBA data • Acs, et al. (1994) find using a production function approach, that small firms have an advantage as recipients of university R&D • Audretsch and Feldman (1996a) find, even after controlling for the extent of geographical concentration of production, that industries where new knowledge is more important tend to cluster more densely  underlines the importance of spillovers in knowledge-intensive industries

44. Research on the SBA database Acs, Z. & Audretsch, D. 1990. Innovation and small firms, Cambridge, Mass., MIT Press Acs, Z., Anselin, L. & Varga, A. 2002. Patents and Innovation Counts as Measures of Regional Production of New Knowledge. Research Policy, 31, 1069-1085. Acs, Z. & Audretsch, D. 1988. Innovation in Large and Small Firms: An Empirical Analysis. American Economic Review, 78, 678-690. Acs, Z., Audretsch, D. & Feldman, M. 1994. R&D Spillovers and Recipient Firm Size. The Review of Economics and Statistics, 76, 336-340. Audretsch, D. B. & Feldman, M. 1996a. R&D Spillovers and the Geography of Innovation and Production. American Economic Review, 86, 630-640. Audretsch, D. B. & Feldman, M. P. 1996b. Innovative Clusters and the Industry Life Cycle. Review of Industrial Organization, 11, 253-273.

45. Other LBIO work • Smaller literature-based surveys-based on searches of trade literature undertaken in recent years: the Netherlands, Austria, Finland, Ireland, and the UK. • Sfinno (Saarinen, 2005 for Finland) was developed as the result of a PhD thesis at Dept of Economic History in Lund, The SWINNO (Sweden) database is currently under construction.

46. Potential issues with LBIO indicators • Selectiontowardssmallerinnovators • Which journals to choose • How to standardize innovation ”height”

47. Econometrics of count data (seealsoslides by Torben) I willherediscusstwotypes of data: • Dependentvalue 0/1 (introduce or not innovation) • Dependentvaluetakesonlyintegervalues (0,1,2,3,… (howmany new innovations/patents Thesetypes of data are referred to as count data. A special sub-group of 2 is when the number is frequentlyzero (e.g. number of visits to a doctor per person in a year)

48. Normality of error terms • Needed to conducthypothesis testing based on t-tests • Usuallyviolated in the context of count data ifOrdinaryLeast Squares used

49. Linearprobabilitymodel (LPM) • LPM is OLS with dependent variable is 0-1. Example: observe a carbought (=1) or not (=0) as a function of income Source: P. Kennedy, A Guide to Econometrics, Malden, MA: BlackwellPubl.

50. Problems of the LPM (=OLS) • Estimatedprobability (= straight line) may be negative or higherthanone • Estimatedresiduals are not randombuttheirsize are systematicallydependent on size of X  a problem referred to as heteroskedasicity  inefficientestimation • Residuals non-normal • Rather, wewould like our regression to look more like the dashedline