Transcript The Spatial Diffusion of rDNA Methods

rK NOWLEDGE
THE SPATIAL DIFFUSION OF
rDNA METHODS
Maryann P. Feldman
Dieter F. Kogler
David L. Rigby
UNC, Department of Public Policy
UCD, School of Geography,
Planning & Environmental Policy
UCLA, Departments of Geography &
Statistics
RESEARCH OBJECTIVES
• To examine the spatial diffusion of a new
technology across U.S. metropolitan areas
• To identify & measure the roles of cognitive
proximity, social proximity & geographical
proximity in the diffusion process
MOTIVATION
• Long history of geographical work on diffusion
– Hagerstrand, Pred, Brown
• Renewed interest in the diffusion of knowledge
connected to uneven development/regional
economic growth (is knowledge fixed in space / how
does it flow/does mobility reduce its value?)
– Polanyi, Griliches, Gertler
• Debates over the relative roles of social proximity &
spatial proximity in knowledge flow
– Jaffe et al., Breschi & Lissoni, Boschma, Singh, Fischer et al.
OUTLINE
• rDNA technology: the Cohen-Boyer patent
• Diffusion of rDNA technology across U.S. cities
• A simple model of diffusion
– Knowledge space & measures of cognitive proximity
– Measuring the social proximity of cities
– Measuring the spatial proximity of cities to rDNA
• Results
• Conclusion
RECOMBINANT DNA
• Cohen-Boyer patent
– Stanley Cohen, Stanford
– Herbert Boyer, UCSF
• Patent application – November 1974
• Patent granted – December, 1980
• Why the time lag?
– Scientific moratorium – Asilomar Conference, 1975
– Supreme Court ruling – Diamond vs. Chakrabarty, 1980
– Bayh-Dole Act – December, 1980
THE OUTCOME
• Perhaps the most successful university
technology licensing program
– 468 firms license technology from Stanford
– Licensing revenues equal $255 million, from $35
billion in worldwide product sales
– Fundamental technology jumpstarts
biotechnology industry
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1,100
100
1,000
90
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400
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10
100
0
0
1976
1979
1981
1983
1985
1987
1989
1991
MSAs with rDNA Inventors
1993
1995
1997
1999
rDNA Patent Applications
2001
2003
2005
Annual CB-Patent Applications
Number of CB-Inventor MSAs
rDNA PATENT APPLICATIONS &
COUNTS OF MSAs WHERE
INVENTORS RESIDE, 1976-2005
KEY CITIES OF rDNA INVENTION
Metropolitan Statistical Area (MSA)
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San Francisco-Oakland-Fremont, CA
Boston-Cambridge-Quincy, MA-NH
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
Washington-Arlington-Alexandria, DC-VA-MD-WV
New York-Northern New Jersey-Long Island, NY-NJ-PA
San Diego-Carlsbad-San Marcos, CA
San Jose-Sunnyvale-Santa Clara, CA
Seattle-Tacoma-Bellevue, WA
Los Angeles-Long Beach-Santa Ana, CA
St. Louis, MO-IL
Chicago-Joliet-Naperville, IL-IN-WI
Sacramento--Arden-Arcade--Roseville, CA
Baltimore-Towson, MD
Houston-Sugar Land-Baytown, TX
Madison, WI
Indianapolis-Carmel, IN
Durham-Chapel Hill, NC
Des Moines-West Des Moines, IA
Oxnard-Thousand Oaks-Ventura, CA
Dallas-Fort Worth-Arlington, TX
rDNA Patent
Applications
1976-2005
Year of First
rDNA Patent
Application
Year When
MSA Reached
10 Applications
1,133
990
691
639
617
585
483
400
260
150
147
127
126
123
122
116
113
97
90
79
1978
1978
1981
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1982
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1976
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1995
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1992
A MODEL OF rDNA DIFFUSION
Development of an
rDNA patent
=
Function of:
Geographical Proximity
Social Proximity
Cognitive Proximity
some covariates
COGNITIVE PROXIMITY
(to Cohen-Boyer)
• Cohen-Boyer is defined as a technological class (1
of 439) in patent records
• Have to find distances between technological
classes
– Look at patent co-classification
– Use probability of co-classification to estimate inter-class
distances
• Visualizations of technological classes & distance
between them
• Cognitive proximity of a city to Cohen-Boyer given
by average proximity (inverse distance) of all
patents in the city to C-B (this not great?)
U.S. KNOWLEDGE SPACE
Chemicals
Computers & Communic.
Drugs & Medical
Electronics
Mechanical
Miscellaneous
1980
435/69.1
U.S. KNOWLEDGE SPACE
Chemicals
Computers & Communic.
Drugs & Medical
Electronics
Mechanical
Miscellaneous
1995
435/69.1
U.S. KNOWLEDGE SPACE
2005
435/69.1
Chemicals
Computers & Communic.
Drugs & Medical
Electronics
Mechanical
Miscellaneous
SOCIAL PROXIMITY
(to C-B)
1. Construct annual lists of coinventors on CB patents
2. Construct lists of co-inventors of
CB co-inventors
1. 366x366 matrix of MSAs
2. Populate with 0s
3. Add 1 to cells i & j when a
pair of CB co-inventors is
located in cities i & j
4. Add 0.5 to cells i & j when
there is a non-CB coinventor relationship in i & j
5. Find centrality of each city
GEOGRAPHICAL PROXIMITY (3 bites)
1.
Bite 1
Bite 2
2.
Take co-ords of each city & build
distance matrix (366x366)
Row sum yields city proximity (inv.
dist.) measure to other cities
Multiply distance matrix for cities
(366x366) by (366x1) vector of
presence/ absence of CB in each
city in each time period yields
overall distance to Cohen-Boyer
Bite 3
X
Take minimum distance
measure to CB from Bite
2 (366x1)
0/1
(366x1)
=
City
Access
to C-B
(366x1)
RESULTS 1:
Event
History
Model
Dependent
variable is time
(# years from
1980) to a city
developing first
C-B patent
RESULTS 2:
Event
History
Model for
Different
Periods
RESULTS 3:
FIXED
EFFECTS
PANEL
LOGIT
Dependent
variable – does
city develops a
C-B patent in
year
Model run with
time-fixed effects
CONCLUSION
• From the hazard model, social proximity &
cognitive proximity exert a positive & significant
impact on the probability that a city develops a
first Cohen-Boyer patent.
• Influence of geographical proximity mixed,
reflecting changing nature of diffusion over time
(first hierarchical, then epidemic)
• City-size and industry R&D have similar positive
impacts, while increases in university R&D reduce
the probability of a C-B patent?