MAPPING CITIES/REGIONS IN KNOWLEDGE SPACE DAVID RIGBY GEOGRAPHY & STATISTICS

Transcription

1 MAPPING CITIES/REGIONS IN KNOWLEDGE SPACE DAVID RIGBY GEOGRAPHY & STATISTICS

2 OUTLINE Motivation Building knowledge spaces Example of Europe Example of Norway Mapping firms/cities/regions in knowledge space Modeling technological diversification (entry/path creation) & examining the roles of: cognitive proximity geographical proximity social proximity

3 81 85 EUROPEAN KNOWLEDGE SPACE ELECTRONICS INSTRUMENTS CHEMICALS BIOTECHNOLOGY INDUSTRIAL PROCESS MACHINERY & TRANSPORT MISCELLANEOUS 06 10

4 EUROPEAN KNOWLEDGE SPACE ELECTRONICS INSTRUMENTS CHEMICALS BIOTECHNOLOGY INDUSTRIAL PROCESS MACHINERY & TRANSPORT MISCELLANEOUS

5 EUROPE (AVERAGE TECHNOLOGICAL RELATEDNESS) PERIOD OVERALL (# PATENTS) (67,133) (94,949) (108,831) (143,244) (141,824) GROUP ELECTRONICS INSTRUMENTS CHEMICALS BIOTECH INDUSTRIAL PROCESS MACHINERY & TRANSPORT MISCELLANEOUS

6 BUILDING KNOWLEDGE SPACE (building technological relatedness measures) All patents classified into 1 or more IPC classes based on knowledge claims (629 IPC main classes) Count frequency with which each pair of IPC classes is found across all patents (for Europe) Standardize by the (square root of the product of the) number of patents in each class of this IPC pair Complications how to weight co invented patents & locate them? how to weight patents with different numbers of knowledge claims?

7 NORWEGIAN KNOWLEDGE SPACE, EARTH/ROCK DRILLING SEPARATIONS FLUIDS/MATERIALS PIPES & FITTINGS PREPARATIONS (PHARMA) MATERIALS TESTING

8 NORWAY NUTS2 (AGDER & ROGOLAND) KNOWLEDGE SPACE, EARTH/ROCK DRILLING

9 NORWAY vs EUROPE PERIOD NORWAY (PATENT SHARE) EUROPE (# PATENTS) (0.4%) (67,133) (0.6%) (108,831) (0.7%) (141,824) (AVERAGE TECHNOLOGICAL RELATEDNESS) PERIOD GROUP ELECTRONICS INSTRUMENTS CHEMICALS BIOTECH INDUSTRIAL PROCESS MACHINERY & TRANSPORT MISCELLANEOUS

10 NORWEGIAN KNOWLEDGE SPACE, CONTAINERS SHIPS & SHIPPING EQPMT INFORMATION STORAGE ACYCLIC/CARBOCYCLIC COMPOUNDS ELECTRIC HEATING/ LIGHTING

11 NORWEGIAN KNOWLEDGE SPACE, SHIPS & SHIPPING EQPMT EARTH/ROCK DRILLING INFORMATION STORAGE PREPARATIONS (PHARMA) MATERIALS TESTING

12 NORWEGIAN KNOWLEDGE SPACE, EARTH/ROCK DRILLING SEPARATIONS FLUIDS/MATERIALS PIPES & FITTINGS PREPARATIONS (PHARMA) MATERIALS TESTING

13 NO06 NO07 NO04 NORWAY NUTS2 REGIONS NO02 NO NO03 NO01

14 TECHNOLOGICAL DIVERSIFICATION & ABANDONMENT Large literature on technological diversification Within firms: JAFFE (1986) Technology space & proximity of firms TEECE, RUMELT, DOSI & WINTER (1994) Corporate coherence ENGELSMAN & VAN RAAN (1994) Mapping technological relationships MALERBA & ORSENIGO (1997) Technological regimes BRESCHI, LISSONI & MALERBA (2003) Knowledge relatedness & technological diversification LETEN, BELDERBOS & VAN LOOY (2007) Technological diversification & coherence Within regions: CANTWELL & VERTOVA (~2000) National technological specialization HIDALGO, KLINGER, BARABASI & HAUSMANN (2007) Product space & development NEFKE & HENNING (2008) Revealed relatedness & industry space BOSCHMA, MINONDO & NAVARRO (2011) Related variety & regional industrial diversification BALLAND, BOSCHMA & KOGLER (2013) Patent space & technological diversification RIGBY (2013) Knowledge space & technological diversification QUATRARO et al

15 TECHNOLOGICAL DIVERSIFICATION & ABANDONMENT 2 Goals: How to measure technological diversity Linking diversity & performance (key within evolutionary research) Is this related to regional renewal (buzz vs pipeline effects?) Modeling entry/exit of firms/regions in/out of different technological classes Aim today: Explore entry of European regions into new technology classes Future work: Measures of the structure of knowledge space & performance

16 MODELING TECHNOLOGICAL DIVERSIFICATION where the binary dependent variable assumes the value 0/1, representing probability region r year t exhibits relative technological specialization in technology class c is a lagged value of the proximity between technology classes where the region does not exhibit technological specialization and those where it does (cognitive proximity) is lagged value number regions with relative specialization in class c each weighted by inverse distance from region r (geographical proximity) is lagged value number regions with relative specialization in class c each weighted by strength co inventor links with region r (social proximity) city, technology class, and year fixed effects added in models (city and class fixed effects drop out of panel model)

17 TECHNOLOGY CLASS ENTRY/ATTACHMENT (cognitive proximity within region based on sum of technological relatedness) TECHNOLOGY NODES ATTACHED UNATTACHED

18 GEOGRAPHICAL NETWORK inverse distance matrix X 0/1 binary mapping relative technological specialization in region r class c 289x x629 SOCIAL NETWORK co inventor links b/t regions X 0/1 binary mapping relative technological specialization in region r class c 289x x629

19 DATA EPO patent data reported to PATSTAT, Approx 1.4 million patent records merged with 2.7 million inventor records (unique inventor IDs) Geographical information to NUTS3, patent technology class information down to sub group level (~10,000 classes)

20 SOME DESCRIPTIVES. sum nutsipcsize Variable Obs Mean Std. Dev. Min Max nutsipcsize sum sumrltdn Variable Obs Mean Std. Dev. Min Max sumrltdn sum geognet Variable Obs Mean Std. Dev. Min Max geognet sum socialnet Variable Obs Mean Std. Dev. Min Max socialnet

21 CORRELATION MATRIX. corr nutsipcsize sumrltdn geognet socialnetw (obs=908905) nutsip~e sumrltdn geognet social~w nutsipcsize sumrltdn geognet socialnet

22 MODEL 1 (ALL EUROPE FE PANEL, DEP VAR NOT TREATED AS BINARY) xtreg proby L.nutsipcsize L.sumrltdn L.geognet L.socialnet i.year, fe cluster(nuts2) Fixed-effects (within) regression Number of obs = Number of groups = F(7,288) = Prob > F = (Std. Err. adjusted for 289 clusters in nuts2) Robust proby Coef. Std. Err. t P>t [95% Conf. Interval] L.nutsipcsiz L.sumrltdn L.geognet 2.98e L.socialnet 4.74e e e e-07 year _cons

23 MODEL 2 (ALL EUROPE FE PANEL, DEP VAR TREATED AS BINARY. xtlogit proby L.nutsipcsize L.sumrltdn L.geognet L.socialnet i.year, fe note: groups ( obs) dropped because of all positive or all negative outcomes. Conditional fixed-effects logistic regression Number of obs = Number of groups = LR chi2(7) = Log likelihood = Prob > chi2 = proby Coef. Std. Err. z P>z [95% Conf. Interval] L.nutsipcsiz L.sumrltdn L.geognet L.socialnet 3.29e e e e-06 year

24 MODEL 3 (EU15 ONLY FE PANEL, DEP VAR TREATED AS BINARY. xtlogit proby L.nutsipcsize L.sumrltdn L.geognet L.socialnet i.year, fe note: groups ( obs) dropped because of all positive or all negative outcomes. Conditional fixed-effects logistic regression Number of obs = Number of groups = LR chi2(7) = Log likelihood = Prob > chi2 = proby Coef. Std. Err. z P>z [95% Conf. Interval] L.nutsipcsiz L.sumrltdn L.geognet L.socialnet 4.55e e e e-06 year

25 CONCLUSIONS Cognitive proximity robust across all models ( buzz ) Geographical proximity unclear what is geographical proximity capturing disembodied spillovers? there is a spatial influence in US city level data is there a border effect in Europe (countries/eu)? is weak geography related to less specialization in European regions compared with U.S. regions? Social influence stronger than spatial effect in US & European data is there a spatial influence on co inventor relationships? different measures of regional co inventor ties General different measures of relative technological specialization in regions take explicit account of spatial autocorrelation in model exit/abandonment model not working well at all? explore more geography within/between country/eu effects mapping inventors and firms/institutions in knowledge space