Prototypes of emerging metropolitan nanodistricts

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					 Prototypes of emerging
 metropolitan nanodistricts
 in the United States and Europe


Philip Shapira1,2, Jan Youtie3, Stephen Carley2

Manchester International Workshop on Nanotechnology,
Society and Policy, September 10, 2008
1Manchester   Institute of Innovation Research, Manchester Business
School, UK
2Georgia Tech School of Public Policy, Atlanta, USA
3Georgia Tech Enterprise Innovation Institute, Atlanta, USA



Contact: pshapira@mbs.ac.uk
Overview

   Prototypes of emerging
    nanodistricts
       Context and theory
       Methods and data
       Results
       Implications
Regional nano emergence:
Prototypes of emerging nanodistricts
   Where is nanotechnology emerging – and how?
       Nanotechnology will be widely diffused
       … but R&D & innovation production will cluster in
        specific regions
            “nanodistricts” = dynamic regional clusters of
             research institutions and firms where
             nanotechnologies are emerging (c.f. Mangematin,
             et al. 2005)
   Why is this important?
       Spatial distribution influences allocation of economic
        benefits from nano (and possibly risks)
       National and regional policymakers seek to foster
        nano innovation – how effective?
       Characteristics of the region may affect the nature
        of nanotechnology development (c.f. Saxenian,
        1994)
          Prototypes of emerging nano districts:
          Issues
             Nanotechnology at an early stage
                  1st of 4 projected phases (Roco, 2004)*
                  i.e. districts are still forming
             But we can anticipate “prototypes”
                  locations which are emerging or promising
                  categorize and analyze them
                  nanotech knowledge development is key
             Convergent/divergent aspects of
              nanotechnology
                  may cause districts to emerge in new locations

* 1. Passive nanostructures (2000+); 2. Active nanostructures; 3. Systems of
nanosystems; 4. Molecular nanosystems (2020+)
Nanodistrict research questions

   Will the economic distribution be more or
    less equally geographically distributed
    (than past technologies)?
   Is there the potential for new geographic
    concentrations of nanotechnology
    research?
   Will nanotechnology-related knowledge be
    able to be exploited?
   How will district construction influence the
    nature of nanotechnology development?
Conceptualizing Nanodistricts:
How will nanodistricts form?
   Path dependency (current leading locations)
       (Fuchs & Shapira 2005, Zucker & Darby 2007)
   New opportunities
       Organizational capital
            Large research facilities (Mangematin 2006)
       Anchor tenant / dominant center of excellence
        (Agrawal & Cochburn 2003; Shapira, Youtie &
        Mohapatra, 1999)
   Human capital
       Star scientist
        (Zucker, Darby & Brewer 1998)
   Nano networks (Davenport & Daellenbach, 2006)
   Regional construction
       Saxenian (1994)
Methods

   Data
       Georgia Tech (CNS-ASU) analysis of SCI/WoS
        publications, 1990-2006 (N=403,000 publication
        records)
       Definition of nano in Porter et al. Refining Search
        Terms for Nanotechnology, (J Nanoparticle Res, 2008)
   Nanodistricts (metropolitan level)
       Location of author institutions
       US: combined statistical areas, MSAs (OMB, 2006)
       EU: OECD Competitive Cities (2006) + NUTS3 +
        bootstrapping
       Publications > 1000, Top 30 (US), 35 (Europe)
       Operationalize regional indicators into cluster analysis
        of nanodistricts
          Prototypes of emerging nanodistricts:
          Measures and Concepts
Measures                                          Concept                      US       Europe

       Total Pubs.                      Scale                                   2954     3018
Pubs/mill. pop.                                                                 2893      1502 *
   Herfindahl                           Anchor tenant                           0.46      0.32 ***
   % nanobio                            Path dependency                       11.5%      9.5% *

       Government                       Facilities                            12.3%     10.7%

University                               Anchor tenant                         81.5%     85.9%
Corporate                                Anchor tenant                         12.1%      3.5% ***
  Early %                               Path dependency                       12.4%      9.1% ***

Late %                                   Path dependency                       60.9%     60.7%

  Authors/article                       Network                                 2.04      2.12
% out-of-metro authors                   Network                               50.9%     61.9% ***
  % highly cited pubs.                  Stars                                  5.9%      2.6% ***

Significance level: ***.01, **.05, *.10; checked symbol = used in cluster analysis
     C A S E          0         5        10        15        20        25
Label           Num   +---------+---------+---------+---------+---------+
Austin            4     
Phoenix          23      
                        
State College   30        
                         
Albany           1      
                           
Atlanta          3            
                                
Gainesville     13               
Madison
Seattle
                19
                28
                       
                       
                        
                        
                                    
                                      
                                           
                                                                 US
Dallas
Philadelphia
                 9
                22
                        
                        
                        
                        
                                    
                                    
                                                    
                                                    
                                                                 Dendogram
Detroit         12        
                                             
ResTri          25      
                                                  
Pittsburgh      24      
                                                  
Houston         14      
                                                  
Cleveland        8                                
Minneapolis     20                                
Albuquerque       2     
                                                    
                                                           
Knoxville        16       
                                                      
Denver          11      
                                                                      
Champaign        6      
                                                                      
Santa Barbara   27        
                                                         
Ithaca          15      
                                                                       
Lafayette       18                                                     
LA              17                    
                                                         
San Diego       26      
                                                                      
Chicago          7      
                                                        
                                                                
SF-SJ           29      
                                                    
Boston           5         
                                             
DC-Balt         10      
                                 
                                      
NY              21      
                       
                    Prototype Nanodistricts:
                    7-Cluster Solution
Cluster           US                                      Europe
TLEAD             SF-SJ, Boston, DC-Balt, Chicago         Paris, London, Frankfurt, Berlin, Rhine-
Technology
      Leaders                                                 Ruhr (Cologne)
UNIV              Ithaca, Champaign, Santa Barbara,       Cambridge, Oxford, Gothenberg,
University
                      Purdue                                 Lancashire (Manchester), Lausanne,
                                                             S. Yorkshire, Karlsruhe, Stuttgart,
                                                             Zurich
GOV               Oak Ridge, Denver, Albuquerque          Grenoble, Toulouse, Lyon, Milan, Rome
Government Labs



DIV               Cleveland, Dallas, Detroit, Houston,    Barcelona, Bas-Rhine (Strasbourg),
Diversified
                     Minneapolis, Philadelphia,              Brussels, Hamburg, Munich,
                     Pittsburgh, Research Triangle           Randstadt, Stockholm, Vienna,
                                                             Hampshire
LENT              Albany, Atlanta, Austin, Gainesville,   Dresden, Kiev, Prague, St. Petersburg
Late Entrants
                     Madison, Phoenix, Seattle, State
                     College
GEOG              Los Angeles, San Diego                  Moscow, Warsaw
Geographical
      Couples

ONEOFF            New York                                Madrid
Leading Nanodistricts by Publications
and Cluster Type
                                                      Legend

                                                    Nanotechnology
                                                     Publications
                                                     1990-2007*
                                                       x 1 000
                                                        1.9 or less
                                                        2.0 – 2.9
                                                        3.0 – 3.9
                                                        4.0 – 4.9
                                                        5.0 – 5.9
                                                        6.0 – 9.9



                                                     Nanodistrict
                                                       Cluster
                                                     Assignments


                                                        DIV
                                                        GEOG
                                                        GOV
                                                        LENT

                                                        ONEOFF
                                                        TLEAD
                                                         UNIV

     Nano publications, 1990-2006 (mid)
     Data & definitions, see Porter et al., 2008.
Leading Nanodistricts by Publications
and Cluster Type
                                                      Legend

                                                    Nanotechnology
                                                     Publications
                                                     1990-2007*
                                                       x 1 000
                                                        1.9 or less
                                                        2.0 – 2.9
                                                        3.0 – 3.9
                                                        4.0 – 4.9
                                                        5.0 – 5.9
                                                        6.0 – 9.9



                                                     Nanodistrict
                                                       Cluster
                                                     Assignments


                                                        DIV
                                                        GEOG
                                                        GOV
                                                        LENT

                                                        ONEOFF
                                                        TLEAD
                                                         UNIV

     Nano publications, 1990-2006 (mid)
     Data & definitions, see Porter et al., 2008.
         Prototype Nanodistricts
         Key Cluster Attributes
Cluster         Attributes                                              US                 Europe
                                                               NE   CA   S.      Mid   NW    S.   E.


Techno          Many pubs, early pubs, low H, high corp                              
leaders         pubs (also patents)
NYC             Most pubs, corporate, nanoelectronics          


Southern Cal    Nanobio, high cites, corporate activity,            
Nanobio         more/out-of-area co-authors
University      University-dominated (high H), early pubs,                           
                few/out-of-area co-authors
Government      Gov lab dominated, more/out-of-area co-                                     
                authors
Late            Late entrants - more university, higher H,                   
                                                                                       
entrants        later pubs                                                             CE

Diverse         Diverse – mid pub size, mid H, more                                   
                corporate pubs, nanobio                                          TX

Madrid          Mix of univ-gov, high pubs, low corporate                                    
                activity
Russia/E.       High pubs, low cites, low corporate activity
                                                                                                    
Europe
          Possible Trajectories
Cluster            Trajectory (anticipations)
Techno leaders     High tech industries, startups, nanobio

NYC                High tech industries, startups, nanoelectronics
Southern Cal       High tech industries, startups, nanobio
Nanobio
University         University spinoffs; corporate/embedded labs?

Government         Government laboratory spinoffs ??

Late entrants      University spinoffs, incumbent /traditional industries using
                   nano
Diverse            High tech and incumbent/traditional industries, startups,
                   nanobio
Madrid             University and government lab spinoffs?

Russia/E. Europe   Human capial movement to other regions?
Corporate Entry: Correlates of
Patenting (US Nanodistricts)

Publications - total                     +
Publications/mill. pop.                   -
% Corporate                              +
% University                             +
% Government                             (-)
% Nanobio                                 -
Herfindahl (1=concentrated; 0=diverse)    -
Highly cited                             (+)
Authors per article                      (+)
% Early publications                     (+)
% Recent publications                    (-)
% out of nanodistrict publications        -
    Conclusions (1)
   Persistence of techno-leaders
       In both US & Europe
       Relationship between bio & nano in leading
        US regions, especially Southern Cal,
        Diverse
BUT
 Nano also offers new geographical
  opportunities
       University-led, Government-led, Middle: US
        & Europe
       Diverse nature of nano
    Conclusions (2)
   Human capital as a factor: mixed
       Citations – all are higher in US, esp.
        Southern California
       Networks – slightly higher in US
        Government-led (hard + soft), but lower
        relative to Europe
   Organizational capital as a factor
       Techno leaders have organizational
        diversity
       Late entrants take monocentric approach
Conclusions (3)

   Implications
       Monocentric approaches work well for
        nontraditional tech regions in near-
        term
       But strengthening human and
        organizational capital, commercial
        applications, demand will be important
        in long-term
Further research

   Add in patenting data
       US: % corporate
        publicationspatenting
   Curious about other business-facing
    nano indicators (that can be
    geocoded) – e.g. announcements,
    start-ups
   Analysis of how district construction
    and dynamics influences the nature
    of nanotechnology development