ECONOMIC DOWNTURNS, TECHNOLOGY TRAJECTORIES AND THE CAREERS OF SCIENTISTS
Eyiwunmi Akinsanmi1 eakinsan@andrew.cmu.edu
Dr. Ray Reagans2 rreagans@mit.edu
Dr. Erica Fuchs1 erhf@andrew.cmu.edu
1
Engineering and Public Policy, Carnegie Mellon University
Sloan School of Management, Massachusetts Institute of Technology
2
Economic downturns conjure up negative imagery in the minds of scholars and laypeople
alike. We picture grainy photos of soup lines that formed during the Great Depression, as
businesses became insolvent and jobs were lost. The National Bureau of Economic Research
defines a recession as a period between a peak and a trough in economic activity in the
chronology of the U.S. business cycle, using measures such as real Gross Domestic Product
(GDP), employment, and real income (NBER, 2010). The NASDAQ’s peak on March 10, 2000,
the benchmark for the burst of the telecommunication bubble, preceded a period of decline in
economic activity. According to the NBER, the U.S. economy was in recession from March
2001 to November 2001 (NBER, 2001).
Productivity, however, does not always decline during an economic downturn. Bresnahan
and Raff find that the Great Depression caused inefficient automobile manufacturing firms to fail
while high-productivity firms later expanded (Bresnahan and Raff, 1991). More recently, US
nonfarm business productivity growth accelerated after the telecom bubble burst, from 2.45%
annual rate during 1995-2000 to 3.51% between 2000:Q2 and 2003:Q4 (Gordon, 2004).
High technology industries can also be affected differently by economic downturns when
compared to the economy at large. During the great depression, employment of research
scientists grew while employment in other occupations collapsed (Mowery and Rosenberg,
1989). In contrast, after the burst of the telecom bubble, technology centers with the highest
concentrations of high-tech employment had the highest unemployment rates (Gittel and Sohl,
2005). After the bubble burst, technology companies with dissimilar growth strategies were also
affected in different ways: those that adopted a “Get Big Fast” strategy were more likely to fail,
while those who followed a more traditional trajectory were more likely to be successful
(Goldfarb et al, 2006).
Despite the troubles of the larger economy, Field argues that the Great Depression was
the most technologically progressive decade of the century in his study of industries ranging
from petro-chemicals to automobiles (Field, 2003). Further, Nicholas and Nabar suggest that
during the Great Depression, uncertainty concerning payoffs to technological development
changed the timing of early stage R&D in some sectors but not in others, and therefore affected
the technology’s trajectory, depending on the underlying sector-level advances in technology
(Nicholas and Nabar, 2009).
This paper studies the effect of the burst of the telecom bubble on the trajectory of an
emerging technology, and the careers of scientists in that industry. We focus on optoelectronics,
a general purpose technology (e.g. Helpman 1998) with applications in energy, biomedical,
telecommunications, computing and military. Leveraging USPTO patents, we analyze the
relationship between an inventor’s pre-bubble characteristics and his productivity post-burst and
thereby the national trend. Past research in technology innovation has used publicly available
patent data to measure productivity (Kapoor and Lim, 2007, Fleming 2007), inventor mobility
(Song et al, 2003, Rosenkopf and Correidora, 2009, Marx et al 2009), and transfer of knowledge
(Almeida and Kogut, 1999, Song et al, 2003, Rosenkopf and Correidora, 2009). In this research
we use patent data to estimate inventors’ pre- and post-bubble burst knowledge capital (here,
number of patents), pre- and post-bubble burst mobility (i.e. number of assignees), pre- and post-
bubble burst technology focus (based on patent classes), and pre-bubble burst career length
patenting in optoelectronics (here time from first to last optoelectronic patent, allowing for a
period of inactivity based on gaps in one’s patent applications). With respect to technology
focus, the dimension on which we differentiate inventors is whether they have pre-bubble patents
in “integration” – an emerging optoelectronics technology that facilitates optoelectronics’
application to non-telecommunications applications. We use logit and OLS models to explore
potentially interesting relationships between inventors’ pre-burst characteristics (knowledge
capital in general or non-integration OE and knowledge capital in integration, career length, and
their interactions) and (1) continuing to patent in optoelectronics and (2) productivity post-burst.
We estimate simple slopes (Aiken, 1991) using the margins command in Stata 11.
Our model finds that on average higher pre-burst knowledge capital and career length
predict higher probabilities of continuing to patent in the field; however, the probability that an
inventor will continue to patent if he has high pre-burst knowledge capital in integration is higher
than if he has pre-burst knowledge capital in general OE. In addition, we find that moves by preburst integration inventors who patent in integration post-burst are associated with higher
productivity while moves by pre-burst general optoelectronics inventors (without patents in
integration) who patent in optoelectronics post-burst are associated with lower productivity. This
differentiated role for inventors who have ever patented in integration supports past work
suggesting that new technologies can experience disproportionate advancement during economic
downturns, and highlights the need for further research into the relationship between sector
specific downturns and the diversification of technologies into new market applications.
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