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Economic Downturns, Inventor Mobility, and Technology Trajectories
Erica R. H. Fuchs
Department of Engineering and Public PolicyCarnegie Mellon University
(from papers co-authored with Akinsanmi, Nugent, Reagans, Ventura, and Yang)
Main take-aways
Superstars Non-stars
2
Economic Downturns, Inventor Mobility, and Technology Trajectories
How do sector-specific business cycles (during bubble and post-burst) affect(1) quantity (patents) and (2) direction (emerging GPT-enabler versus rest of the field)
of innovation?Are inventor innovation outcomes in part explained by the inventor's
mobility into and out of telecom? Do star inventors respond in a different way from non-stars?
3
(Akinsanmi, Reagans, Fuchs (2015) Seeing Rainbows While Others Flee: How innovation in the most advanced technologies grew after the burst of the telecommunications bubble. Carnegie Mellon University Working Paper. )
Data: US Optoelectronics Inventors Pre-Burst USPTO Population
– >70,000 US OE Inventors and >175,000 OE Patents total– Patent Data missing (1) inventors who move if they don’t patent
afterwards (2) Career beyond active patenting (3) Inventor background
CV Subsample– 729 US OE Inventors and 12,400 Patents– Contacts provided by SPIE, OSA and IEEE’s Photonics Society
Oral Histories
INVENTORS TARGET SAMPLE CV SAMPLE RESPONSE RATE
Top 1.5% by Total Patents 760 237(78 overlap-Superstars)
30%; 73% of those reached
Top 1.5% by Patents/Year 680 233 34%; 82% of those reached
All Inventors in Emerging GPT Enabler 900 182 20%, 54% of those reached
RANDOM SAMPLE of Non-Emerging GPT Enabler Inventors in OE
1250 180 15%; 83% of those reached
Difference in Difference, Two-stage model
4(Akinsanmi, Reagans, Fuchs (2015) Seeing Rainbows While Others Flee. CMU Working Paper. )
consulting
Hand Coded >2000 firms into 20 market applications in each year
divers. w/ telec
academia
all telecom
defense
divers. w/o telec
1956
con
sent
dec
ree
limiti
ng B
ell L
abs
oper
atio
n
1962
: Exp
ansi
on 1
beg
ins
1969
: Rec
essi
on 1
beg
ins
1971
: Exp
ansi
on 2
beg
ins
1973
: Rec
essi
on 2
beg
ins
1976
: Exp
ansi
on 3
beg
ins
1980
: Rec
essi
on 3
beg
ins
1983
: Exp
ansi
on 4
beg
ins
1984
: Bre
ak u
p of
Bel
l Lab
s
1990
: Rec
essi
on 4
beg
ins
1992
: Exp
ansi
on 5
beg
ins
1996
: Bel
l Lab
s di
vide
d ag
ain
2000
: Rec
essi
on 5
beg
ins
2002
: Exp
ansi
on 6
beg
ins
2008
: Rec
essi
on 6
beg
ins
PRE- TELECOM BUBBLE
1940 1950 1960 1970 1980 1990 2000 20100
20
40
60
80
100
120
140
160
180
Year
Nu
mb
er o
f in
ven
tors
in
eac
h m
arke
t ap
pli
cati
on
BUBBLE
POST-BURST
defense
5(Akinsanmi, Reagans, Fuchs (2015) Seeing Rainbows While Others Flee. CMU Working Paper. )
Bubble burst disproportionately reduced innovation in rest of field compared to emerging GPT-enabler (e.g. Field, 2011)
Super-stars advance the emerging GPT enabler during resource-constrained parts of the business cycle
Their efforts build on the efforts of non-stars during less constrained times
Bubble: Herd mentality by non-stars into emerging GPT enabler
Post-burst: Super-stars see tech. opportunity despite downturn; Majority of emerging GPT-enabler inventors leave field,
dislocated from their IP, stop innovating (Yang et al 2015, Yang & Fuchs WP)
Preliminary Findings
6 (Akinsanmi, Reagans, Fuchs (2015) Seeing Rainbows While Others Flee. CMU Working Paper. )
The Super-stars: In their own words
Quote
“By late 2000 we knew the bubble had burst. I was at Intel Capital and I knew it had burst because we had ratcheted down all the terms on our term sheets. And I went and started a firm the next year. It was like going into the eye of the storm. I was either being an entrepreneur or being very stupid. But I had a novel technology I believed in, I had access to some capital, and I could assemble a world class team” – M. L.
Outcome
Acquired in 2003
“It was a natural evolution to start a company… I didn’t think about bubble or burst. I had a niche technology for short-distance data-com and that market continued to grow…year by year ” – Anon.
Acquired in 2004
“You’re starting a new company … in the start of the worst crash … unaware of whether you’re going to come out of it okay or not. But we decided it was a good challenge. It was a very exciting time to try and do that. The competition at that time, all they were doing was trying to protect against a downside of their current business as opposed to [investing] in the future of where the business needed to go because their revenues were dropping. They were cost-cutting. They were trying to save programs. And they weren’t able to invest in new technology” – D. W.
Initial Public Offering in 2007
7(Akinsanmi, Reagans, Fuchs (2015) Seeing Rainbows While Others Flee. CMU Working Paper. )
The Non-Superstars: Majority leave field, stop innovating
“These days… you cannot find a research-type job. There are very very very few. It’s not like the old days that companies spend a lot of money on research. It’s more… development engineering.”
“It was a tough time in the job market and so I was happy to just find a job. I… [joined] the yield group [in a computing company]… it was my first non-optical-electronics job.”
“They offer[ed] some positions in the headquarters but nobody took it…. families are here, right,… and the positions they were offering were not related to what we did before.”
(Fuchs, Nugent, Yang (2015) Gains from Others Losses: Technology Trajectories and the Global Division of Firms. Research Policy. Accepted. )
The Non-Superstars: Dislocation of Inventors from their IP
“So… every company I’ve ever been with, you sign your rights away to any inventions you make. That’s straight up, you sign away for your salary.”
“…they shut down the foundry, 'cause they can purchase the same function although it's bigger or more bulky optics, discrete optics…we always think about whether we can bring the same technology we developed into some real use, because it has value…. but it’s protected by the patents.”
“[At first after being acquired] I kept filing lots of patents… other companies wanted to license the patents but they made it very difficult to license…. so I would write patents, but then nobody could use the inventions because [Firm] didn’t make that type of product and other people couldn’t license them.”
9 (Fuchs and Yang (2015) The Dislocation of Inventors from their IP. CMU Working Paper. )
The Challenges Measuring Mobility
CVs: disentangles relationship between mobility &
patenting; inventor characteristics; limited by sample; HARD! (Ge et al 2015, Akinsanmi et al WP)
Past disambiguation methods: 10-22% error on available sample closest to full USPTO (splitting), systemic biases (context)
Supervised learning method: maintains under 3% errors on all available
samples (Ventura et al 2015)
Magnitude of different sources of error?Disambiguation? Endogeneity?
10
Smal
l Sam
ple
Smal
ler
and
Lar
ger
Sam
ples
Thank You
11
Main Take-Aways
Theory: Economic downturnsBubble burst disproportionately reduced innovation in rest of
field compared to emerging, general purpose technology (GPT) - enabler (e.g. Field, 2011)
Super-stars advance the emerging GPT during resource-constrained parts of the business cycle (Akinsanmi et al WP)
Majority of emerging GPT inventors leave field, dislocated from their IP, stop innovating (Yang et al 2015, Yang and Fuchs WP)
Methods: Measuring mobilityPast disambiguation methods: 10-22% errors on available
sample closest to full USPTO, systemic biasesSupervised learning method: maintains under 3% errors on
all available samples (Ventura et al 2015)
CVs: disentangles relationship between mobility & patenting; limited by sample (Ge et al 2015, Akinsanmi et al WP)
Tbd: disambiguation or endogeneity greater source of error?12
A Sector-Specific Economic Downturn
1998 1999 2000 2001 2002 2003$0
$5
$10
$15
$20
$25
$30
$35
$40
$45
$50
Year
Actual vs. Forecast U.S. Fiber-Optic Market Sizes
Actual Demand
Forecast Demand
(Cahners Business Information 2000, Turbini & Stafford 2003)
Telecommunications Bubble Burst(March 2000)
Year
US
D (
Bill
ion
s)
1976 1981 1986 1991 1996 2001 20060
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Pat
ents
sub
clas
s/P
aten
ts s
ubcl
ass
in 2
002
Emerging GPT Enabler Takes Off while Rest of Field Plateaus
Rest of Field(N = 184274)
Emerging GPT Enabler(N = 3237)
Data up to 2010 Telecommunications Bubble Burst
Patenting in the Rest of Field rises steadily, reaches its peak in 2002, and then declines Patenting in the emerging enabling technology rises quickly from the mid 1990s, peaks in
2002 and subsequently plateaus
Patenting in Optoelectronics
14
Economic downturns have disparate effects on different firms, people and technologies
Firms
Great Depression most technologically progressive decade of the century, Field ‘03
People
Innovation Trajectories
Economic DTs in general Great Depression Post Telecom Bubble Burst
• Start-ups more likely to fail than larger firms (Geroski & Gregg, 1997)
• New firms less likely to get VC funding (Paik et al, 2013)
• Firm survival dependent on pre-downturn productivity (Bresnahan and Raff, 1991)
• Firm survival dependent on pre-downturn growth strategy (Goldfarb et al, 2006)
• Jobs created low-paying and temporary (Bowlus, 1993, Davis et al 1996)
• Firms more likely to train incumbents while reducing recruitment of new employees (Brunello, 2009)
• Employment of research scientists grew (Mowery and Rosenberg, 1989)
• Technology centers had the highest unemployment rates (Gittel and Sohl, 2005)
• Newest process and product innovations continue to be created (Caballero and Hammour, 1994, Shu, 2012)
• Firms invest in product innovation rather than process innovation (Brechicci et al, 2013)
• 1930s experienced very high rates of innovation (Field, 2011)
• Timing of early stage R&D changed, changing technology trajectories (Nicholas and Nabar, 2009)
?15
Stars may be better able to benefit from mobility
16
Mobility • Mobile inventors more productive than non-mobile (Hoisl 2007).
• Are moves to similar or different contexts most advantageous? - Heterogeneously networked team more productive than homogenous
(Reagans and Zuckerman, 2001);
- Knowledge transfer increases with technological distance (Rosenkopf & Almeida, 2003, Song, Almeida & Wu, 2003)
- For acquisitions, high routine overlap and moderate skill overlap (Kapoor &
Lim, 2007) and prior communication (Agarwal et al, 2012) leads to higher productivity
Stars Star involvement matters: close ties between academics and firm scientists needed for commercialization (Zucker & Darby, 1998)
Star arrival leads to 38% increase in department productivity (Agarwal et al). Star death leads to lasting decline in collaborators’ quality-adjusted publication rates (Azoulay et al, 2010, Oettl, 2012).
Stars less likely to leave their firms (Campbell et al, 2011, Carnahan et al, 2012),Stars who move draw level with or overtake non-movers in productivity
(Hoisl, 2009).
Case: Optoelectronics (OE) Industry
Intersection between electronics and photonics; conversion of electric signals to light signals and vice-versa
Photons:– higher information carrying capacity – lower power consumption
Innovation in OE first driven by telecom but central to advances in computing, biomedical, energy and military
General Purpose Technology “has the potential to be extremely pervasive and used as inputs by a wide range of sectors in the economy” (Helpman and Trajtenberg, 1998)
17
Emerging GPT Enabler: OE Integration
OE Integration: incorporates multiple devices onto a single chip, enabling reduced size and allowing OE access to broader set of markets (Eng, 2010)
OE integration inventors: opportunity to switch market applications while leveraging same technical competency
Traditional Architecture Integration
Ferry, 2010
Source: LuteraSource: U. Wash Source: Stanford U
18
Hypotheses
H1. During the bubble: Inventors who move into telecom increase their patenting in both the
rest of field and the emerging GPT enabler.
H2. Post-burst: Inventors who move out of telecom increase their patenting in the
emerging GPT enabler but not in the rest of field (e.g. Field 2011)
Star inventors who move out of telecom disproportionately increase patenting in emerging GPT enabler but not in rest of field (e.g. Hoisl 2009)
19
Execution: Dr. Eugene Arthurs, Krisinda Plenkovich and the SPIE organizationThe Optical Society of America and IEEE’s PhotonicsSam Ventura & Rebecca NugentWillis Chang, Carl Glazer, Farjad Zaim, Sabrina Larkin, Neha Nandakumar,
Angela NgEarly feedback:
Jeff Furman, David Hounshell, and M. Granger MorganShane Greenstein, Scott Stern and Michael Piore Lee Branstetter, Brian Kovak, and Fiona MurrayCarliss Baldwin, James Utterback, Rajshree Agarwal, Scott Stern and the other
participants of INFORMS, ISA, CCC and WTIC conferencesFinancial support:
National Science Foundation Science of Science and Innovation Policy Program
Schlumberger Foundation
Acknowledgements
20
sample overlaps
21