Which Stars Should You Reach For?
Firm Innovation and the
Mobility of Scientific Stars∗
Alexander Oettl†
University of Toronto
Rotman School of Management
105 St. George Street
Toronto, Ontario M5S 3E6
May 6, 2006
Abstract
This paper provides preliminary estimates of the relationship between the mobility
of star (high performing) scientists and firm level innovative output in the form of
patenting. In this study, I distinguish between current star scientists and former star
scientists. A current star scientists is identified as a scientist whose patenting output in
the previous five years is greater than the mean plus 2 standard deviations of a similar
cohort consisting of all patenting individuals during the same period. A former star
scientist is a scientists that has met this same criterion earlier, but not in the most recent
five year. Results suggest a strong statistically and economically significant effect of the
arrival of both current and former star scientists on firm inventive patenting. Former
star scientists have a much stronger effect than current star scientists on patenting
behavior, wherein the arrival of a single former star leads to an increase in patenting
by 23.5%, while the arrival of a single current star scientist leads to an increase in
patenting by approximately 5%. I attempt to control for unobserved heterogeneity and
omitted variable bias by implementing firm level fixed effects, 2SLS IV estimation, and
an inverse mills ratio to account for self selection bias.
JEL Classifications: J62, 031, 033
Keywords: star scientists, human capital, patenting, labor mobility, firm innovation
∗
Preliminary Work. Special thanks to Ajay Agrawal for immeasurable advice and guidance. I would also
like to thank Joel Baum, Sampsa Samila and seminar participants at the University of Toronto Strategy
Workshop for helpful comments. All errors and omissions are my own.
†
Alexander.Oettl04@rotman.utoronto.ca
1
Introduction
The fact that the majority of scientific work is produced by a small minority of scientists
is by no means a new discovery (Lotka 1926). Zucker and Darby (1996) found that the
top 0.8% of GenBank contributors, accounted for an astonishing 17% of all contributions.
Similarly, the top 1% of inventors account for 15.6% of all United States Patent and Trademark Office (USPTO) patents.1 Given this skewed distribution of inventive activity, and
the central role that patenting plays in many highly innovative industries, it is surprising
that not more attention has been placed on examining these star scientists.
In a number of seminal pieces, Zucker and Darby (Zucker and Darby, 1996; Zucker
and Darby, 1997; Zucker, Darby, and Brewer, 1998; Zucker and Darby, 2001) examine the
extent to which academic star scientists directly influence the locations of new biotechnology
startups. The extent to which new firms are founded, they argue, is a function of a highly
skilled group of individuals – stars, and their mobility patterns. Dasgupta and David
(1994) too acknowledge the importance of both high skill individuals and labor mobility in
transferring knowledge between universities and firms.
A separate, but equally related literature has primarily focused on the extent to which
labor mobility a) influences a firm’s ability to learn (Song, Almeida, and Wu 2003), b) affects
organizational competency change (Henderson, 1994; Lacetera, Cockburn, and Henderson,
2004; Tzabbar, 2005) and c) aids in the regional transfer of knowledge (Almeida and Kogut
1999). Despite the important roles that both high-skilled individuals, and human capital
mobility play in developing firm resources, no work has explored firm patenting behavior
through this nexus of mobility and star scientists.2
It is the goal of this paper to provide empirical estimates of the effect of star scientist
mobility on firm innovation rates. I model the change in a firms rate of innovation as a
1
2
Author’s calculations.
A notable exception is Lacetera, Cockburn, and Henderson (2004).
1
function of the arrival of new star scientists. Furthermore, I distinguish between the types
of star scientists by placing each star into one of two categories: a current star, or a former
star. Through this disaggregation of stars I am able to provide more fine-grained analysis
on the effects of star scientist recruitment benefits for a sample of firms. I test two main
hypotheses. One, that the arrival of both current and former star scientists has a positive
and significant impact on firm innovation. And two, because of possible differences in
objectives and mentoring abilities, former stars will have a larger impact on firm patenting
than current stars.
Using a panel dataset of 284 firms across 25 years, I attempt to control for unobserved
firm level heterogeneity and firm self selection effects. Preliminary findings support the
hypothesis that star movers do indeed have a large and statistically significant effect on
firm innovation rates. In addition, former stars have a statistically significant larger impact
on firm patenting than current stars. Furthermore, while these results do hold across a
number of robustness checks, the reader is still cautioned in their interpretation of these
results, as the endogeneity of firm hiring, while addressed, can still be greatly improved
upon.
The paper proceeds as follows. The next section outlines some of the salient literature
on firm innovation, the role of mobility and the effects of star scientists, in particular rising
and falling stars. Section 3 outlines the methodology of this study, followed by the reporting
of preliminary results. A more detailed discussion of the caveats relating to this research
along with possible remedies is provided in section 5.
2
Literature and Theory
The resource-based view of the firm framework, views the firm as a collection of resources
that provide sustained competitive advantages (Barney 1991). These resources can be
2
viewed as assets, skills, or simply general strengths (Wernerfelt 1984). These resources,
however, are largely static, and in order to grow or acquire new resources, firms must look
outside their boundaries. The two most common modes of resource obtainment is through
the formation of strategic alliances (Eisenhardt and Schoonhoven, 1996; Mowery, Oxley, and
Silverman, 1998) and diversification (Silverman 1999). A third mode of acquiring resources
is through the hiring of individuals (Aldrich and Pfeffer, 1976; Argote and Ingram, 2000).
Recent work that has focused on the role of labor mobility and its relationship with
firm resources has found that labor mobility contributes to a firm’s search practices and its
technological positioning (Tzabbar 2005), its ability to adopt science-based drug discovery
(Lacetera, Cockburn, and Henderson 2004), and to a firm’s ability to learn from recently
departed scientists (Oettl and Agrawal 2005). More importantly, however, this work highlights the salience of labor mobility as a conduit for knowledge transfer between former and
new locations, as well as the human capital benefits provided by these moving individuals,
in particular high performing individuals.
High performing individuals – stars – characterize many scientific industries. In a recent study by Audretsch, Aldridge, and Oettl (2006), the top 20% of research oncologists
captured over 68% of total National Cancer Institute (NCI) funding between the years of
1998 and 2004.3 Indeed, skewed distributions of scientific research output is becoming an
increasingly well documented phenomenon (Narin and Breitzman, 1995; Ernst, Leptien,
and Vitt, 2000). Azoulay and Zivin (2005) measure the spillovers generated by stars onto
their co-authors through research collaboration. They too find strong evidence of highly
skewed publishing and patenting behavior amongst their sample of academic stars in the
life sciences. The notion of peer worker productivity is not new (Hamilton, Nickerson, and
Owan 2003), but the ability for firms to achieve a form of competitive advantage from high
performing individuals runs contrary to neoclassical economic theory.
3
over $5.5B of a total $8B awarded.
3
Labor market theorists would argue that firms cannot gain potential spillovers from
star scientists for in a competitive labor market, the stars would recognize their additional
value, and capture any rents associated with their spillover potential (Hirshleifer 1998).
Placed within the context of the resource-based view, however, this proposition seems more
plausible. Since firms are distinct in their resource allocations, certain firms may benefit
disproportionately from certain star scientists (Barney 1986).
If a disproportionate amount of scientific output is produced by star scientists, and
labor mobility is a key source of firm learning, it follows that mobile star scientists lead to
increased firm learning. With increased firm learning comes increased inventive activity,
which leads to greater innovation rates (Cohen and Levinthal, 1989; Cohen and Levinthal,
1990). Furthermore, due to the skewed nature of the generation of inventive activity, star
scientists should provide greater levels of spillovers than non-star scientists. Formally, I
hypothesize that:
Hypothesis 1a: The arrival of a current star scientist is positively associated with an
increase in firm level innovation.
Hypothesis 1b: The arrival of a former star scientist is positively associated with an
increase in firm level innovation.
While both current and former star scientists affect firm innovation, which of the two
contribute more to firm learning? I view firm innovation as dependent on firm learning, so
that firms with disproportionate levels in learning will recognize disproportionate changes
in inventive activity, and subsequently innovative activity. Both a current and former star
scientist are characterized as high performing, yet are at different career stages. A former
star scientist may be more willing to share knowledge, and be less competitive with fellow
scientists than a current star, who is in direct competition with fellow infirm scientists. As
such, former star scientists may provide greater spillover opportunities than current stars,
4
and as such provide for greater learning throughout the firm. Formally:
Hypothesis 2: Former star scientists have a larger impact on firm level innovation than
current star scientists.
This paper contributes to the literature in two ways. First, the existing star literature
has been slow to differentiate between differing types of star scientists. Conversely, the
labor mobility literature has not always been able to focus on the firm as the central unit
of analysis. This paper attempts to extend both of these two literatures.
3
Methodology
In this section I will outline my empirical strategy, including data sources, variable construction and main econometric specification. Details on the construction of my mover
sample is provided, as well as descriptive statistics of the sample as a whole.
3.1
Movers and Shakers
The purpose of this study is to examine the influence of the arrival of star scientists on the
patenting behaviors of their new colleagues. The difficulty associated with these types of
studies is a) identifying movers, and b) classifying movers as stars. I make use of patent
data to achieve both of these tasks. First, I examine an individual’s patenting history
across time and identify a move as the occasion when the same inventor is located in two
separate firms and/or geographic locations on two different patents. Second, I identify
stars by comparing five year moving averages with that of the entire patenting population
during the same five year period for a particular technology class. By constructing a five
year patenting cohort for each year and technology class, I am able to distinguish between
stars in different technologies, and thus control for the heterogeneous patenting behaviors
of various technologies. I identify a scientist as a star, if the scientist’s patent count in his
5
main technological area for the previous five years is more than two standard deviations
above the mean of the entire population during the same time period and across the same
technology class. The construction of these measure will now be discussed in more detail.
Movers are identified through the examination of inventor names listed on all patents
filed (and subsequently awarded) through the USPTO between the years of 1975 and 2004.
I first create a list of inventors that contains all inventors which had patented for two
different companies during their career. That is, if someone had filed a patent in 1984 and
the assignee listed on the patent was Advanced Micro Devices, and filed another patent in
1986 where the assignee was Intel, I add this individual to my list as a potential mover.
Since I will be using US centric control data, I limit the list of movers to those that had
moved within the United States only. Furthermore, while the exact timing of the move is
unknown, I can presume with a relatively high level of certainty, that the individual was at
the new firm at the time of the patent filing. As such, I do not know if the inventor arrived
prior to the year that the first patent at the new firm was filed, but I know that it was not
afterwards.
This approach runs the risk of misidentifying two separate individuals as the same
person, due to the sharing of the same name - a type II error. Conversely, I am at risk
of encountering type I errors, whereby I miss inventors that file patents with a range of
spelling permutations. In this paper, I do not normalize inventor names to mitigate this
type I error, and as a result, my sample should be viewed as a more restrictive set of
movers.4 Furthermore, since it is unlikely that name spelling permutations are correlated
with patenting activity, this type I error should not introduce any systematic bias, and as
such can be treated as measurement error attributed noise.
The issue of type II errors – wrongfully identifying two separate individuals as the same
– is more serious, however. In order to provide some reassurance that all of the patents
4
For information on name normalization, I turn you to the interesting, but preliminary work being done
by Manuel Trajtenberg.
6
authored by the inventors in my list of movers can be properly attributed to them, I add the
restriction that patents must be in related fields (Oettl and Agrawal, 2005; Singh, 2005).
I achieve this by allowing all patents that achieve one of the following three conditions:
1) a match at the WIPO defined international classification subclass level, 2) a match at
the primary 3-digit US classification class level, or 3) a match between a patent’s primary
3-digit classification, and any of the secondary 3-digit classifications.5 Furthermore, I only
analyze moves that took place within the US.
Applying this restriction algorithm, I identify a total of 49,907 direct move instances,
from a sample of approximately 3.2 million patents.6 These movers are used in identifying
the cross-sectional units that will makeup my dataset, and will be more fully explored in
the next section. But first, the stars.
Now that the move instances have been identified, it is time to identify the stars of
these 49,907 move instances.
There are 39,596 inventors that generated these 49,907
move instances, suggesting that the average moving inventor moves approximately 1.25
times.7 Table 1 provides some descriptive statistics on the mover population. From this
sample, of 39,596 moving inventors, the mean patents per inventor is 6.45, whereas the
average inventor has only 3.2 patents.8 This result is not too surprising, since the approach
I use to identify movers only applies to individuals with at least two patents, indicating
that my mover sample will already be above the average with respect to patenting activity.
5
There are 4,011 unique classifications at the international subclass level, and 436 unique 3-digit utility
patent US classifications.
6
I differentiate between direct moves and indirect moves. If an inventor moves from firm A to firm B to
firm C, we would identify two direct moves: A→B and B→C, but also an indirect move from A→C. I am
not interested in these indirect moves.
7
There are far fewer than 39,596 unique names however. In identifying inventors, I associate each
name with the individual’s primary patenting area. As such, I distinguish between John A. Smith who
predominantly patents in the field of Electrical Devices, and the John A. Smith who predominantly patents
in the field of Optics. I use the Hall, Jaffe, and Trajtenberg (2001) technology subcategory in which the
individual most heavily patents as their predominant field.
8
Once again, due to the high level of skewness amongst patenting, the mean values may be misleading.
The median number of patents from the moving inventor sample is 4, where the median inventor across all
patents is 1.
7
While table 1 provides descriptive statistics between the population of inventors and
movers, table 2 examines the distribution of patenting between stars and non-stars. The
observant reader will notice that the summation of the observations of stars and non-stars
will not equal the number of observations for all patenters presented in table 1. Since the
identification of a star scientist is contingent on the inventor being on the tail end of the
patenting distribution of his own field, each inventor is identified by not only his name, but
also his primary field. As can be seen, however, star scientists are much more productive
than non-star scientists, not only in the average number of patents awarded, 12.25 and 1.7,
respectively, but also with respect to the mean number of patents possed by each sample: 9
and 1, respectively. Furthermore, the distribution of patents by stars is much more skewed
than non-stars, indicating that even within the skewed ranks of high-skilled stars, output
levels are far from homogenous.
Table 3 examines the differences between moving stars and non-moving (static) stars.
As can be seen, a small percentage of all stars (7.4%) ever move. That being said, moving
stars are only slightly more productive than static moves, where the average moving star
patents just under 15 times, while the average static stars patents just above 12 times in
his lifetime. It should be noted, however, that scientists with more patents are more likely
to be identified as movers using my identification algorithm, and as such, this result should
not be too surprising.
I now turn my attention to the unit of analysis, which is constructed using the total
mover sample of 39,596 individuals.
3.2
Unit of Analysis
The purpose of this study is to empirically estimate the influence of current and former
stars on the patenting behavior of a cross section of firms. In order to control for the
effects of endogenous firm choices, omitted variables biases, and unobserved heterogeneity,
8
I construct a panel dataset across 25 years of data between 1978 and 2002. My cross
sectional units – firms – are chosen as follows. First, I identified the set of firms that
received at least one star mover from the set of 49,907 move instances. Second, I match
this set of firms to compustat CUSIP numbers, which I use to obtain accounting control
data. By including compustat accounting financial data, I also am able to determine at
what times firms enter and leave my sample.
After imposing these two restrictions upon my data, I am left with a sample of 284
firms across 25 years, wherein 21,186 incoming and outgoing moves take place, of which
1,215 moves are by stars (Table 4). As a result, my final dataset consists of (25 × 284)
7,100 firm-year observations. Due to data limitations and the lagging of the main independent variables, my panel dataset becomes unbalanced and my total observations drop to
4,754 under the full regression specification. Table 6 provides some additional descriptive
statistics on the top 25 star receiving firms in the dataset.
The 284 firms within my sample, are from a wide variety of industries. As industry
variation can greatly explain profitability, as well as performance (Rumelt, 1999; McGahan
and Porter, 1998), I attempt to control for industry specific variation through the inclusion
of firm level fixed effects, and by clustering standard errors (where applicable) on 4 digit
SIC industry codes.
3.3
Variable Definition and Operationalization
Dependent Variable
Patentsit The dependent variable of this study is the count of the patents applied for in
your t, and that were subsequently awarded, since I can only observe successful patent
applications. Patent data has long been used in studies of innovation with reasonable
success (Griliches,1990; Stuart, 2000; Ahuja and Katila, 2001; Ziedonis, 2004) That being
said, patent data is not without its shortcomings. Some inventions are not patentable, and
9
others are purposefully kept out of the public domain. Nonetheless, patent data are still
reliable measures of firm innovation, albeit noisy ones (Mowery, Oxley, and Silverman 1996).
Since the empirical purpose is to estimate the patenting gains that accrue to the organization that the star has joined, I remove from the patent count the star’s own patents,
so as to only measure the changes in patenting behavior of the other scientists at the firm,
that is, the mover’s new colleagues. Furthermore, the exclusion of the mover’s own patents
from the dependent variable is crucial in properly estimating the magnitude by which the
organization at large learns through the arrival of this new star scientist, and is not biased
by the patenting rate of the newly hired star.
Independent Variables
The CurrentStarsit−1 variable is a count of the number of current stars that moved to
firm i at t − 1. Conversely the FormerStarsit−1 variable is the number of falling stars
that moved to firm i at time t − 1. The variable Intra-Firm CurrentStarsit−1 is a count
of the number of current stars that moved from one branch of firm i to another branch at
t − 1. Mapping mover locations to metropolitan statistical areas (MSAs), I am only able to
identify branch moves if the mover changed MSAs. Intra-Firm FormerStarsit−1 applied
the former star criterion to intra-firm moves as just mentioned. An intra-firm mover would
be an individual moving from IBM San Francisco to IBM Austin, for example.
Intra-MSA CurrentStarsit−1 are current stars that changed firms, but still reside in
the same MSA, while Intra-MSA FormerStarsit−1 are former stars that changed firms,
but still reside in the same MSA. A scientist moving from IBM San Francisco to Dell San
Francisco constitues an intra-firm move. Inter-Firm CurrentStarsit−1 are current stars
that changed firms, as well as changed MSAs. Inter-Firm FormerStarsit−1 are former
stars that changed firms and MSAs. A scientist moving from IBM San Francisco to Dell
Austin, would be an inter-firm move.
10
Control Variables
The variables outlined in this section attempt to control for extraneous effects that might
also be influencing a firm’s patenting behavior. It may be that the effect of the arrival of star
scientists is proportional to the level of other scientists hired during the same time period.
As such, the variable InboundMoversit−1 and OutboundMoversit−1 is operationalized
as the number of non star scientists that arrive and depart firm i at time t − 1, respectively.
In addition, one of the strongest predictors of a firm’s patenting behavior is its R&D
spending (Hall, Griliches, and Hausman 1986). As such, I include the variable R&Dit−1
as the level of research and development expenditures in year t − 1. In addition, I control
for the size of the firm with the variable Employeesit , as well as R&D Sizeit which is the
number of unique inventors that filed for a patent (that was subsequently awarded) at firm
i in year t. Ageit is defined as the number of years elapsed since firm i first patented. It
crudely captures firm i’s years of patenting experience.
The R&Dit−1 and Employeesit control variables are obtained from the COMPUSTAT
database, and are entered into the regression specification as natural logs due to their highly
skewed nature. As a result, the coefficient estimates can be interpreted as an elasticity.
Lastly, a yearly time trend is included as well to control for macro level shocks that could
affect the patenting rate of all firms in my sample. Results were unchanged when year
dummies were included instead of a yearly time trend.
3.4
Descriptive Statistics
In table 7, I present basic descriptive statistics of my dependent, independent and control
variables. As can be seen, the average number of patents that are applied for in a year
is just shy of 37, however, with the median at 2 patents per year, it is quite clear that a
few large firms are accounting for the majority of patenting activity. Likewise, there is on
average 0.12 current star movers that arrive at a firm in my sample at any given year. This
11
value drops greatly for former star scientists where there is on average 0.01 star scientists
recruited per year. Once again, these values are greatly skewed as one firm hired 10 stars
in one year.9
3.5
Estimation
With the dependent variable being a count of the number of successful patent applications
by firm i in year t, it is not uncommon for a firm to not receive a patent every year. In fact,
in my sample, no patents were awarded in 1,077 firm-years. As a result, a high proportion
of observations will have values of 0. Furthermore, due to the count nature of patent counts,
a poisson distribution is the most appropriate way of modeling these data (Greene 1997).
The poisson regression model dictates that the dependent variable is a draw from the
poisson distribution with a parameter λi which is a function of the vector of regressors xi .
The model can be formulated as follows:
P rob(Yi = yi ) =
e−λi λyi i
, yi = 0, 1, 2, ...
yi !
As a result, the expected number of events per period can be given as:
E[yi |xi ] = V ar[yi |xi ] = λi
0
= eβ xi
(1)
The parameters in the vector β can now be estimated by maximizing the log-likelihood
equation:
ln L =
n
X
−λi + yi β 0 xi − ln yi !
i=1
9
IBM hired 10 star scientists in 1995, where 8 stars were intra-firm transfers, and 2 were intra-msa moves.
12
As can be seen from equation 1, the poisson assumption of first and second moment
equality is quite strong. If this assumption is violated, the parameters will still be consistently estimated, but the standard errors will be underestimated, making it difficult to
conduct any form of hypothesis testing (Cockburn and Henderson 1996). As the moment
conditions are rarely equal in this type of setting, an alternate specification can be used
following the work of Hausman, Hall, and Griliches (1984) whereby the specification allows
for the variance to be proportional to the mean, rather than equal.10 That is, the HHG
model allows for overdispersion (Wooldridge 2002). As such, the regression specification to
be estimated is as follows:
E[Pit |Mit−1 , Xit , δt , ζi ] = exp(α0 Mit−1 + β 0 Xit + δt + it )
(2)
whereby the conditional expectation of the number of successful patent application Pit
is a function of the number of different types of star movers Mit−1 , a vector of control
variables Xit−1 , year time effects δt , and the disturbance parameter it . In addition, the
expectation is conditioned on a set of firm level fixed effects ζi . A positive value in the
vector α indicates that the mobility of star scientists have a positive effect on a firm’s
patenting level.
This basic specification still suffers from potential endogeneity in the form of omitted
variable bias and self selection bias. These concerns along with potential remedies are
offered in the next section.
Reverse Causality and Omitted Variable Bias
One of the concerns associated with these types of studies is the issue of omitted variable
bias. For example, both patenting behavior and star scientist recruitment may be correlated
with a firm’s specific R&D strategy. I attempt to control for this type of scenario with
10
A Lagrange Multiplier test verifies the presence of overdispersion by rejecting the pure poisson model.
13
three methods. First, I propose to estimate equation 2 by means of a conditional fixed
effects negative binomial regression, wherein parameter estimates are obtained by analyzing
within group variation. The upside is that this conditional fixed effects model allows the
error terms to be correlated with the explanatory variables, and by using within group
variation, all time-invariant heterogeneity will be controlled for. The downside with this
approach, of course, is that all time-invariant heterogeneity will still be present, as well
as cross-sectional units that experience no variation in patenting across the sample time
period will be dropped. A hausman test was conducted to determine the appropriateness
of a random effects model. The appropriateness of the random effect model was rejected,
indicating that the error term is correlated with the regression covariates. Consequently a
fixed effects model is more appropriate in this case.
Second, I use lagged independent variables to predict variation in patenting. By incorporating a lag structure, I am able to explicitly model my predicted causal relationship,
and in doing so, attempt to lessen the effect of patenting potentially driving star scientist recruitment. The mover variables that I employ, however, are quite noisy. Because of
the characteristics of the mover sample that I am working with, I am unable to precisely
determine the year at which a mover arrives at a firm. My only indication of a move is
through patent filings at a new firm. Thus, while I may not be able to determine if the
mover arrived earlier, I know that the mover never arrived after filing. As such, while this
inability to exactly measure the arrival time adds noise to my measurement, it biases results
downward.
Third, following the work of Lacetera, Cockburn, and Henderson (2004), I run an instrumental variable approach attempting to control for the endogeneity of the star scientist
variable. As with any type of IV estimation, picking an appropriate instrument is much
more of an art than a science. In this case, I was looking for an instrument that was correlated with star scientist movement, but not patenting. One such variable which meets
14
the previous two criteria is the average age of the incoming group of star scientists.11 I use
the lagged average age of incoming current stars as an instrument for current stars and the
lagged average age of incoming former stars as an instrument for former star. Converting
the dependent variable of patent counts to a non integer variable (ln[patents + 1])(Pakes
and Griliches 1980), I conduct two stage least squares (2SLS) estimation to correct for
endogeneity.
Self Selection Bias
Of additional concern is the possibility of firms self selecting into a specific hiring strategy
(Shaver, 1998; Hamilton and Nickerson, 2003; Lacetera, Cockburn, and Henderson, 2004).
To correct for possible self selection issues, I first run a first stage probit based selection
model whereby I presume hiring is a dichotomous choice, which can be modeled as:
Pr(Hireit = 1|Xit ) = Φ(γ 0 Xit )
where the probability of hiring someone is a function of the control variables included in
equation 2, where Φ is the standard normal cumulative distribution function (Greene 1997).
Using the predicted values of this first stage regression, I construct the following ratio (λ),
known as the inverse mills ratio:
λ=


0

 φ(γ 0Xit +ζi )
Φ(γ Xit +ζi )



−φ(γ 0 Xit +ζi )
{1−Φ(γ 0 Xit +ζi )}
if Hire = 0
(3)
if Hire = 1
I include the inverse mills ratio in a second stage linear OLS regression modeled using
a variant of the specification in equation 2. A statistically significant inverse mills ratio is
an indication of selection bias.
11
ρ(current age,patenting) = 0.37, ρ(current age,current stars) = 0.78; ρ(former age,patenting) = 0.06,
ρ(former age,former stars) = 0.93
15
4
Results
Table 8 presents conditional fixed effect negative binomial regression results for specification
2, where the dependent variable is the number of successful patents applications in year
t. The natural logs of R&D expenditures employee count in year t are included in all
specifications, except for the baseline specification in column 1. Firm fixed effects are
included in all specifications, however, along with a year trend variable.
Column 1 presents my base specification whereby I model changes in patenting behavior
as a function of current and former stars.12 Column 2, provides a much more reasonable
estimates of the impact of stars on patenting by controlling for R&D spending and firm size
(employees). Since the mobility measures used throughout this study are entered in levels,
the coefficient can be interpreted as the percentage change in the dependent variable, given
a one unit increase in the independent variable. As such, the arrival of one current star is
associated with an increase in patenting of 15.5%, while the arrival of one former star is
associated with an increase in patenting by 24.3%.
In columns 3 and 4, I include controls for both inbound and outbound non-star movers,
as well as firm age. Controlling for total non-star hirings and departures, greatly reduces
the impact of current stars, but leaves former stars largely unchanged. Column 5, controls
for the quality of the patent being generated by including a control for the number of
citations that the dependent variable has received. This inclusion increases the strength of
the current star variable. Finally, column 6 presents the full specification, with all controls,
whereby in addition to firm citations, I include the R&D size of firm i (in thousands). This
variable can be interpreted as follows. The addition of 1,000 more R&D staff will increase
patenting by 79.2%. Doing so, wipes out the effect of forward citations – the quality of
the knowledge being patented. The arrival of one current star increases firm patenting by
12
Different lag structures were used for the mobility variables. The one period lags had the most predictive
power. Conversely, for R&D data and firm size, there was little variation between one year lags and
contemporaneous values. As such, I use R&D and firm size data from year t.
16
5.1%, while the arrival of one former star increases firm patenting by 23.5%. Furthermore,
the probability of the coefficient estimates for current and former stars being equal is about
1%.
Table 9, extends the full specification shown in Table 8, column 6 (excluding citations
due to their minimal explanatory power in the full model) by including interaction effects
between firm age, R&D size, Inbound movers, R&D spending and employees. Throughout
all specifications, the affect of current stars on patenting is significant at the 1% level or
lower, while the magnitude of the arrival of one current star on patenting varies between
changes by 11.1% and 64.2%. Former Stars are significant at the 5% level or below across
all specifications, with magnitude varying between 26.8% and 88.0%.
Generally, the arrival of a current stars has a larger effect on patenting at younger firms
(Firm Age), firms with a small R&D function (R&D Size), when fewer non-star movers
enter the firm, at firms with smaller R&D expenditures, and at smaller firms (employees)
[Columns 1 - 5]. Including all interactions into one specification (Column 6), however, tells
a different tale. All of the interactions with former stars yield insignificant results. This
result may not be too surprising as the number of former stars across the entire sample is
very low (64), and thus without much variation across this variable, the interactions are in
many cases highly insignificant. Current stars, however, still contribute more to younger
firms. A possible explanation is that the technology trajectories of younger firms are less
rigid, and as such have a greater probability of being influenced by a star scientist than an
older firm. Conversely, current stars have a stronger influence on patenting at firms with
lower R&D spending. One possible explanation might be that R&D spending and hiring
current stars are substitute strategies. I will further explore this effect with robustness
checks in Table 10. Lastly, current stars are associated with hiring patenting at larger
firms. Larger firms possibly have stronger support services in place to more productively
make use of star scientists than smaller firms. I present robustness checks to these results
17
in tables 10 and 11.
Table 10 presents negative binomial fixed effects results on a number of split samples.
First, I construct the within firm mean value of R&D size, R&D spending, and employees.
I then estimate the mean of this within firm mean across all firms, and split the sample
accordingly. General findings include that R&D firms with average R&D sizes below the
sample’s mean realize larger effects from the arrival of current and former stars than firms
with above average R&D sizes. Both results are statistically significant.
The arrival of current star scientist, however, has a statistically significant negative effect
on R&D spending for firms below the mean level of R&D spending within the sample. In
fact, the arrival of one current star to a firm with below average R&D expenditures results
in a decrease in patenting by 18.5%. The effects, however, of the arrival of former stars, is
still unchanged from the baseline specifications presented in table 9.
Current and Former star scientists have no effect on firms with total employees below
the sample average, providing additional support to the positive interaction result between
current stars and employees in table 9. In addition, inbound non-star scientists have no
significant effect on firm patenting, while the departure of non-star scientists has a negative
impact.
Table 11 presents additional robustness checks by modeling the relationship between
firm patenting and the arrival of star scientists using varying estimation techniques. Columns
1 and 2 estimate the relationship without fixed effects using a negative binomial and poisson specification, respectively. The impact of current stars increases in magnitude from
similar negative binomial fixed effect models in table 8, while the magnitude of the former
star coefficient stays largely unchanged. Across both specifications, however, the overall
statistical significant decreases, albeit all estimates are still below the 10% level.
Unfortunately, the poisson family of estimators are not as flexible as ordinary least
squares (OLS) with respect to conducting diagnostic regression analysis. As such, I convert
18
the dependent variable to a natural log in columns 3 through 6. In order to not drop
all observations where the dependent variable is 0, I follow the general technique in the
literature of taking the log of the dependent variable plus 1 (Acemoglu and Linn 2004).
While this does lead to biased results, it does allow me to conduct instrumental variable
regressions, and other two stage estimation techniques, which under the poisson count
model is extremely difficult due to the exponential, versus additive, functional form of the
regressors. Furthermore, I allow for clustering at the 4 digit sic code level to control for
any cross-sectional error term correlations that may result from industry specific effects.13
Column 3 serves the purpose of establishing my base model, but with the newly transformed dependent variable. Comparing these parameter estimates to those obtained from
the conditional fixed effects negative binomial regressions in table 8, we see that the estimates are similar in magnitude, along with generally similar standard errors. Firm Age
and the year trend variable become insignificant, while the employee variable enters the
specification positively.
Column 4 tests for omitted variable bias by conducting a two stage least squares (2SLS)
estimation, using the average age of the incoming current stars and former stars as instruments. Using these instruments, the current star effect increases in magnitude (above the
linear base specification in column 3), while the former star effect decreases slightly.
Columns 5 and 6 attempt to capture any self selection issues that may arise from
firms selecting which star scientists to hire. Following work in the strategic management
literature on the treatment of endogenous independent variables (Shaver, 1998; Hamilton
and Nickerson, 2003), I construct an inverse mills ratio as defined in equation 3. The
statistical significance of the inverse mills ratio (λ), indicates a self selection bias. The
parameter estimates (and standard errors) are largely unchanged from the specification in
column 3, indicating either that bias of self selection is negligible, or that the (first-stage)
13
Tests were also run using clustering at the 2 digit SIC level. The 4 digit SIC codes produced larger
standard errors, and so I report these, erring towards a downward bias in significance.
19
selection model is misspecified. More appropriate determinants of firm star scientist hiring
strategies will be pursued in the future with the aim of increasing the traction of using an
inverse mills ratio to account for self selection.
In table 12 I disaggregate the current and former star mover variables into the types
of moves they constitute. A move can be one of three types: 1) an intra-firm move (intermsa), 2) an intra-msa (inter-firm) move, and 3) an inter-msa, inter-firm move. Column
1 present a baseline (aggregate) specification for the purpose of comparison. Intra-Firm
moves as modeled in column 2 appear to have negligible impact firm patenting. A highly
probably explanation is that firms do a good job of distributing knowledge throughout their
organizational boundaries, so that the movement of an individual within these boundaries
provides a limited increase in value/learning. Column 3 examines moves wherein the arriving star previously resided in the same city, but at a different firm. Both current and
former stars have a statistically significant impact on firm patenting, and in magnitude
larger to what was estimated from intra-firm moves only. Column 4 examines the impact
of inter-firm and inter-firm and inter-msa star moves. Since knowledge diffusion is largely
geographically localized ((Jaffe, Trajtenberg, and Henderson 1993), it is not surprising that
an inter-firm move arriving from a different geographic regions would provide additional
learning opportunities to the destination firm, and consequently provide a larger impact on
patenting. Column 5 includes all three types of moves into one specification, wherein the
results remain largely unchanged from the results shown in columns 2, 3 and 4.
5
Discussion and Conclusion
This paper has attempted to demonstrate a relationship between the movement of star
scientists and firm innovation rates, as measured by patent output. While a strong relationship appears to exist between the arrival of current and former star scientists and firm
patenting behavior, caution is advised in interpreting these results for a number of reasons.
20
First, because of the aggregate nature of my level of analysis, I am not able to directly
account for individual level effects that may be largely driving this relationship. Song,
Almeida, and Wu (2003) for example find that movers to new firms are more likely to
provide knowledge transfer when the moving scientist possesses technological knowledge in
a distant area from the firm’s core technological area. While this study is agnostic to the
form by which knowledge is transferred, if firms are cognisant of the performance benefits
from hiring individuals with distant knowledge, then this can introduce a selection bias.
Understanding this relationship and firm hiring practices better in general, will allow me
to control for this selection and hopefully obtain unbiased estimates of the effect of star
mobility.
Second, the use of patenting as a tool for identifying star scientists is dangerous when
analyzing its effects on patenting behavior. While I minimize the endogeneity present by
only examining the patenting rates of non star scientists, a more exogenous selection strategy would be preferred. Academic publication count would be a good substitute criterion
(Azoulay and Zivin 2005), but in doing so I would limit the number of firms I am able to
analyze, as academic scientists typically only migrate to a handful of industries. Isolating the study to a single industry, however, would allow me to control for inter-industry
heterogeneity, and may provide for a more optimal research setting.
Third, while I have argued a causal relationship between star movement and subsequent
patenting, without controlling for potential self selection and omitted variable bias, the
relationship may be simply spurious. For example, if firms only hire star scientists that
provide positive patenting spillover benefits, then I have little to say on the effect of the
mobility of a randomly selected star scientist on firm patenting. I attempt to control for this
self selection by constructing an inverse mills ratio. While the coefficient on the inverse mills
ratio is statistically significant, indicating a selection bias, my results remain unchanged.
A probable reason for this is a misspecified selection model, which was used to construct
21
the inverse mills ratio. A more appropriate hiring selection model will surely improve
the predictive power of this model. As for omitted variable bias, it is quite plausible that
some external factors are influencing both star scientist hiring decisions as well as patenting
behavior, such as the recruitment of a new director of R&D. I employ a number of techniques
in an attempt to reduce this bias. First, I attempt to control for time invariant unobserved
influences by using firm fixed effects. Second, I include R&D expenditures in all equations
to control for large shifts in R&D spending, which may be highly correlated with new star
scientist hirings and patenting output. Third, I use the average age of star movers as an
instrument for star scientist mobility, with the belief that age is positively associated with
mobility trends, but not innovative output. 2SLS results continue to provide significant
coefficients, but the model can be greatly improved with a more appropriate instrument.
As such, these results should be viewed as descriptive, and serve as a motivation for a
more extensive research program into the relationship between stars and economic growth.
22
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26
Table 1: Movers versus Population
Descriptive Statistics
Table 2: Stars versus Non-Stars
Descriptive Statistics
Table 3: Moving Stars versus Static Stars
Descriptive Statistics
All Patenters
Movers
Stars
Non-Stars
Moving
Static
2,045,025
3.22
6.84
19.85
1
1
1
1
1
3
7
11
29
39,596
6.45
8.15
7.36
2
2
2
3
4
7
13
19
38
110,769
12.25
11.42
7.59
3
4
5
6
9
14
22
30
56
2,169,510
1.70
1.37
3.23
1
1
1
1
1
2
3
4
7
8,216
14.86
13.29
5.18
4
5
6
8
11
17
27
36
66
102,553
12.04
11.23
7.91
3
4
5
6
9
14
22
29
55
Obs
Mean
Std. Dev.
Skewness
1%
5%
10%
25%
50%
75%
90%
95%
99%
Obs
Mean
Std. Dev.
Skewness
1%
5%
10%
25%
50%
75%
90%
95%
99%
Obs
Mean
Std. Dev.
Skewness
1%
5%
10%
25%
50%
75%
90%
95%
99%
Table 4: Move Breakdown
Table 5: Average Inventor Patenting Across Time
Count
Percentage
Non-Star Scientists
Incoming
Outgoing
Total (Non-Stars)
9,983
9,988
19,971
47.1%
47.1%
94.3%
Star Scientists
Incoming Current Stars
Incoming Former Stars
Outgoing Current Stars
Outgoing Former Stars
Total (Stars)
1,151
64
977
90
1,215
5.4%
0.3%
4.6%
0.4%
5.7%
Total Moves
21,186
100.0%
Time Period
1976-1980
1981-1985
1986-1990
1991-1995
1996-2000
Total Patents
Inventors
Patent Avg.
356,620
346,169
521,900
729,526
1,027,218
212,571
199,446
289,051
382,368
511,907
1.68
1.74
1.81
1.91
2.01
Table 6: Top 25 Firms where Stars Haved Moved To
Current Formers
Inbound Outbound
Stars
Stars Total Stars Movers
Movers
Rank
Name
4 Digit SIC
Patents
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
INTERNATIONAL BUSINESS MACHI
GENERAL ELECTRIC
EASTMAN KODAK
DOW CHEMICAL
MICRON TECHNOLOGY
XEROX
INTEL
LUCENT TECHNOLOGIES
TEXAS INSTRUMENTS
E I DU PONT DE NEMOURS
ALLIED SIGNAL
ADVANCED MICRO DEVICES INC
BAKER HUGHES
BAYER
APPLIED MATERIALS
SCHLUMBERGER TECHNOLOGY
PFIZER INC
CHIRON
GENERAL MOTORS
HEWLETT PACKARD
BECTON DICKINSON
MICROSOFT
SUN MICROSYSTEMS
MOLEX
BRISTOL MYERS SQUIBB
7370
9997
3861
2821
3674
3577
3674
7373
3674
2820
3728
3674
3533
2800
3559
1389
2834
2834
3711
3570
3841
7372
3571
3678
2834
39,352
25,312
16,915
7,847
10,415
12,551
9,066
7,546
11,247
11,567
5,258
7,098
1,463
960
3,108
1,939
2,917
552
9,480
9,706
2,032
3,580
4,245
1,226
1,975
76
52
25
25
22
23
22
20
18
15
14
15
10
11
11
11
10
10
10
10
9
9
9
7
8
0
2
2
0
1
0
0
1
0
1
1
0
4
3
2
1
1
0
0
0
0
0
0
1
0
76
54
27
25
23
23
22
21
18
16
15
15
14
14
13
12
11
10
10
10
9
9
9
8
8
955
509
177
206
122
200
353
320
262
227
150
118
69
87
99
70
107
28
87
203
86
125
117
23
176
1212
645
224
316
69
229
233
293
361
257
205
114
44
41
57
59
96
30
178
232
84
62
62
19
136
207,357
383,405
54.1%
452
1,087
41.6%
20
64
31.3%
472
1,151
41.0%
4,876
9,983
48.8%
5,258
9,988
52.6%
Top 25 Total
Total
Proportion Assigned to Top 25
Table 7: Summary Statistics and Correlation Matrix
Variable Name
1 Patents
Obs
8950
Mean
36.807
Std. Dev.
139.13
Min
0.0
Max
3866.0
1
2 Current Stars*
8592
0.117
0.47
0.0
10.0
0.59
3 Former Stars*
8592
0.007
0.09
0.0
2.0
0.06
0.08
4 Intra-Firm Current Stars*
8592
0.040
0.27
0.0
8.0
0.57
0.74
0.04
5 Intra-Firm Former Stars*
8592
0.000
0.02
0.0
1.0
0.03
0.02
0.31
0.02
6 Intra-MSA Current Stars*
8592
0.057
0.28
0.0
5.0
0.31
0.72
0.07
0.19
0.02
7 Intra-MSA Former Stars*
8592
0.005
0.07
0.0
2.0
0.05
0.09
0.85
0.05
0.00
0.07
8 Inter-Firm Current Stars*
8592
0.020
0.15
0.0
3.0
0.25
0.47
0.05
0.14
0.00
0.13
0.07
9 Inter-Firm Former Stars*
8592
0.002
0.04
0.0
1.0
0.02
0.00
0.48
-0.01
0.17
0.01
0.00
-0.01
10 ln R&D*
4754
3.955
2.15
-4.3
9.1
0.36
0.26
0.08
0.20
0.03
0.18
0.07
0.13
0.03
11 ln Employees
5457
1.988
2.28
-6.9
6.8
0.29
0.16
0.05
0.15
0.03
0.08
0.04
0.06
0.04
0.78
12 Firm Age
8950
11.515
10.78
0.0
50.0
0.28
0.19
0.07
0.17
0.03
0.10
0.05
0.09
0.04
0.45
0.43
13 Inbound Movers
8950
1.115
4.19
0.0
112.0
0.87
0.61
0.07
0.57
0.04
0.34
0.07
0.25
0.01
0.37
0.26
0.27
14 Outbound Movers
8950
1.116
4.79
0.0
154.0
0.87
0.60
0.05
0.62
0.03
0.30
0.05
0.22
0.01
0.34
0.25
0.30
0.90
15 R&D Size (1,000s)
8950
57.216
225.23
0.0
6163.0
0.96
0.59
0.05
0.60
0.03
0.29
0.05
0.21
0.02
0.37
0.29
0.28
0.89
* One Year Lag
2
3
4
5
6
7
8
9
10
11
12
13
14
0.91
Table 8: Conditional Fixed Effects Negative Binomial Regressions, 1978-2002
Dependent Variable
Patents by firm excluding the star's patents
(1)
(2)
(3)
(4)
(5)
(6)
Current Stars
0.224
(.015)***
0.155
(.014)***
0.147
(.014)***
0.042
(.015)***
0.052
(.014)***
0.051
(.011)***
Former Stars
0.450
(.098)***
0.243
(.088)***
0.254
(.084)***
0.248
(.076)***
0.247
(.075)***
0.235
(.072)***
0.112
(0.020)***
0.234
(.020)***
0.234
(.020)***
0.223
(.019)***
0.234
(.020)***
-0.039
(.019)**
-0.104
(.020)***
-0.098
(.020)***
-0.093
(.020)***
-0.101
(.020)***
0.026
(.003)***
0.027
(.003)***
0.027
(.003)***
0.029
(.003)***
Inbound
Movers
0.054
(.003)***
0.053
(.003)***
0.043
(.003)***
Outbound
Movers
-0.027
(.002)***
-0.029
(.002)***
-0.046
(.003)***
0.039
(.004)***
0.001
(.004)
ln R&D
ln Employees
Firm Age
Citations
(1,000)
R&D Size
(1,000)
0.792
(.061)***
Year
0.056
(.002)***
0.010
(.003)***
-0.020
(.004)***
-0.023
(.004)***
-0.020
(.004)***
-0.025
(.004)***
Intercept
-130.536
(3.651)***
-19.812
(4.985)***
39.320
(7.951)***
45.207
(8.043)***
39.043
(8.027)***
47.905
(8.096)***
8,256
4,517
4,517
4,517
4,517
4,517
Number of Groups
344
284
284
284
284
284
Fixed Effects
Firm
Firm
Firm
Firm
Firm
Firm
current=former
0.0241
0.3317
0.2156
0.008
0.0104
0.0115
Log Likelihood
-23,608.09
-15,882.26
-15,838.99
-15,673.41
-15,637.25
-15,570.35
1,920.98
0.0000
793.60
0.0000
910.33
0.0000
2,042.78
0.0000
2,286.82
0.0000
2,844.25
0.0000
Observations
†
Chi^2
Prob > Chi^2
Note: Standard errors in parentheses
Tests the equality between current stars and former stars (P-value)
*,**,*** significant at 10%, 5% and 1% levels, respectively
†
Table 9: Conditional Fixed Effects Negative Binomial Regressions, 1978-2002
Dependent Variable
Patents by firm excluding the star's patents
(1)
(2)
(3)
(4)
(5)
(6)
Current Stars
0.244
(.042)***
0.111
(.017)***
0.125
(.019)***
0.537
(.055)***
0.227
(.035)***
0.642
(.064)***
Former Stars
0.490
(.199)**
0.268
(.109)**
0.378
(.114)***
0.880
(.313)***
0.486
(.177)***
0.863
(.361)**
ln R&D
0.229
(.019)***
0.231
(.020)***
0.229
(.020)***
0.240
(.019)***
0.226
(.020)***
0.248
(.020)***
ln Employees
-0.099
(.020)***
-0.100
(.020)***
-0.098
(.020)***
-0.103
(.020)***
-0.093
(.020)***
-0.114
(.020)***
Firm Age
0.031
(.003)***
0.029
(.003)***
0.028
(.003)***
0.030
(.003)***
0.030
(.003)***
0.030
(.003)***
Inbound
Movers
0.041
(.003)***
0.037
(.003)***
0.044
(.003)***
0.043
(.003)***
0.041
(.003)***
0.041
(.004)***
Outbound
Movers
-0.042
(.003)***
-0.039
(.003)***
-0.039
(.003)***
-0.044
(.003)***
-0.044
(.003)***
-0.040
(.004)***
R&D Size
(1,000)
0.746
(.048)***
0.798
(.047)***
0.688
(.053)***
0.787
(.047)***
0.793
(.047)***
0.748
(.073)***
Current Stars
x Firm Age
-0.0071
(.0015)***
-0.0055
(.0016)***
Former Stars
x Firm Age
-0.0122
(.0070)
-0.0042
(.0091)
Current Stars
x R&D Size
-0.0254
(.0058)***
-0.0098
(.0137)
Former Stars
x R&D Size
-0.1099
(.1919)
0.4007
(.3143)
Current Stars
x Inbound Movers
-0.0019
(.0004)***
-0.0001
(.0008)
Former Stars
x Inbound Movers
-0.0176
(.0104)*
-0.0164
(.0177)
Current Stars
x R&D spending
-0.0636
(.0073)***
-0.0873
(.0134)***
Former Stars
x R&D spending
-0.1076
(.0501)**
-0.0857
(.0800)
Current Stars
x employees
-0.0380
(.0074)***
0.0546
(.0134)***
Former Stars
x employees
-0.0728
(0.0447)
-0.0130
(.0663)
Year
-0.025
(.004)***
-0.026
(.004)***
-0.025
(.004)***
-0.027
(.004)***
-0.026
(.004)***
-0.025
(.004)***
Intercept
48.249
(7.954)***
50.306
(8.091)***
49.322
(8.092)***
52.470
(7.986)***
51.176
(8.065)***
49.681
(7.951)***
4,517
4,517
4,517
4,517
4,517
4,517
Number of Groups
284
284
284
284
284
284
Fixed Effects
Firm
Firm
Firm
Firm
Firm
Firm
-15,556.70
-15,560.04
-15,557.16
-15,531.54
-15,557.00
-15,519.33
2,881.03
0.0000
2,973.48
0.0000
2,945.52
0.0000
2,936.33
0.0000
2,881.37
0.0000
2,997.48
0.0000
Observations
Log Likelihood
Chi^2
Prob > Chi^2
Note: Standard errors in parentheses
*,**,*** significant at 10%, 5% and 1% levels, respectively
Table 10: Robustness Regressions
Conditional Fixed Effects Negative Binomial Regressions, 1978-2002
Dependent Variable
Patents by firm excluding the star's patents
R&D Size
R&D Spending
Employees
> group mean < group mean > group mean < group mean > group mean < group mean
Current Stars
0.044
(.011)***
0.070
(.028)**
0.049
(.012)***
-0.185
(.030)***
0.056
(.012)***
-0.027
(.035)
Former Stars
0.178
(.076)**
0.338
(.119)***
0.190
(.075)**
0.213
(.119)*
0.192
(.077)**
-0.152
(.123)
ln R&D
0.268
(.035)***
0.108
(.025)***
0.205
(.032)***
0.342
(.030)***
0.117
(.026)***
0.250
(.030)***
0.0480
(.040)
-0.078
(.025)***
-0.032
(.032)
-0.073
(.028)***
0.025
(.034)
0.031
(.037)
Firm Age
0.049
(.004)***
0.014
(.005)***
0.054
(.003)***
-0.014
(.006)**
0.056
(.003)***
-0.020
(.008)***
Inbound
Movers
0.034
(.003)***
-0.081
(.012)***
0.041
(.003)***
-0.041
(.013)***
0.043
(.003)***
-0.007
(.017)
Outbound
Movers
-0.035
(.003)***
-0.039
(.013)***
-0.043
(.003)***
-0.047
(.014)***
-0.045
(.003)***
-0.044
(.022)**
R&D Size
(1,000)
0.700
(.044)***
21.313
(.544)***
0.755
(.047)***
6.243
(.456)***
0.781
(.048)***
3.991
(.377)***
Year
-0.062
(.006)***
-0.013
(.007)*
-0.058
(.005)***
0.014
(.008)*
-0.058
(.005)***
0.051
(.009)***
120.736
(11.688)***
24.848
(13.314)*
114.150
(10.231)***
-28.558
(14.995)*
114.033
(9.177)***
-101.066
(17.577)***
1,468
3,049
2,396
2,121
2,922
1,595
71
213
130
154
149
135
Firm
Firm
Firm
Firm
Firm
Firm
Log Likelihood
-7,432.55
-7,421.48
-9,818.62
-5,530.77
-11,335.58
-4,018.75
Chi^2
Prob > Chi^2
2,215.91
0.0000
4,195.09
0.0000
2,339.65
0.0000
1,561.10
0.0000
2,243.63
0.0000
1,413.27
0.0000
ln Employees
Intercept
Observations
Number of Groups
Fixed Effects
Note: Standard errors in parentheses
*,**,*** significant at 10%, 5% and 1% levels, respectively
Table 11: Robustness Regressions II
NB, Poisson, and OLS Regressions: 1978-2002
Dependent Variable
Patents by firm excluding the
star's patents
(1)
(2)
ln (patents + 1) †
‡
(3)
(4)
(5)
(6)
Current Stars
0.066
(.028)**
0.103
(.021)***
0.054
(.022)***
0.081
(.036)**
0.053
(.023)**
0.043
(.022)*
Former Stars
0.247
(.126)*
0.258
(.121)**
0.340
(.108)***
0.291
(.128)**
0.330
(.105)***
0.362
(.100)***
ln R&D
0.251
(.072)***
0.407
(.064)***
0.164
(.059)***
0.164
(.061)***
0.163
(.058)***
0.162
(.059)***
ln Employees
-0.0210
(.046)
-0.120
(.073)
0.249
(.054)***
0.248
(.056)***
0.247
(.055)***
0.253
(.056)***
Firm Age
0.029
(.006)***
0.047
(.007)***
0.010
(.018)
0.010
(.019)
0.009
(.019)
0.009
(.019)
Inbound
Movers
0.013
(0.021)
0.049
(.006)***
0.043
(.017)**
0.042
(.018)**
0.043
(.017)**
0.043
(.017)**
Outbound
Movers
-0.073
(.015)***
-0.058
(.009)***
-0.079
(.017)***
-0.080
(.018)***
-0.079
(.016)***
-0.079
(.016)***
R&D Size
(1,000)
5.761
(2.115)***
0.939
(.249)***
2.389
(.798)***
2.383
(.821)***
2.375
(.778)***
2.391
(.779)***
Year
-0.027
(.007)***
-0.053
(.011)***
-0.013
(.015)
-0.013
(.0157)
-0.011
(.016)
-0.011
(.016)
-0.082
(.017)***
-0.200
(.046)***
λ (IMR)
Intercept
Observations
Estimation
Log Likelihood
R^2
54.217
(13.211)***
107.713
(22.038)***
28.018
(29.947)
28.425
(30.906)
23.859
(31.330)
23.705
-31.7250
4,684
4,684
4,684
4,684
4,419
4,419
Negative
Binomial
No FE
Poisson
No FE
OLS
Firm FE
2SLS
Firm FE
OLS Firm
FE Current
Star IMR
OLS Firm
FE Former
Star IMR
-19,086.71
-142,029.04
N/A
N/A
N/A
N/A
N/A
N/A
0.643
0.889
0.640
0.638
Note: Standard errors adjusted for clustering on industry (SIC) in parentheses
These specifications include a dummy set to 1 when patents = 0 per Pakes and Griliches (1980)
‡
Current and Former Stars are instrumented by the average age of these incoming and former stars
Wald and F-tests of joint insignificance are all rejected.
*,**,*** significant at 10%, 5% and 1% levels, respectively
†
Table 12: Move Types
Conditional Fixed Effects Negative Binomial Regressions, 1978-2002
Dependent Variable
Patents by firm excluding the star's patents
Baseline
(1)
Current Stars
(All)
0.051
(.011)***
Former Stars
(All)
0.235
(.072)***
(2)
(3)
(4)
(5)
Current Stars
Intra-Firm
0.030
(.016)*
0.017
(.017)
Former Stars
Intra-Firm
0.195
(.249)
0.065
(.258)
Current Stars
Inta-MSA
0.100
(.022)***
0.1050
(.023)***
Former Stars
Intra-MSA
0.232
(.090)***
0.1940
(.091)**
Current Stars
Inter-Firm&MSA
0.149
(.045)***
0.163
(.043)***
Former Stars
Inter-Firm&MSA
0.3643
(.154)**
0.3660
(.159)**
ln R&D
0.235
(.019)***
0.241
(.019)***
0.238
(.019)***
0.242
(.019)***
0.237
(.020)***
ln Employees
-0.102
(.020)***
-0.105
(.020)***
-0.105
(.020)***
-0.107
(.020)***
-1.040
(.020)***
Firm Age
0.029
(.003)***
0.029
(.003)***
0.029
(.003)***
0.029
(.003)***
0.029
(.003)***
Inbound
Movers
0.042
(.003)***
0.044
(.003)***
0.043
(.003)***
0.042
(.003)***
0.041
(.003)***
Outbound
Movers
-0.046
(.003)***
-0.046
(.003)***
-0.045
(.003)***
-0.045
(.003)***
-0.046
(.003)***
R&D Size
(1,000)
0.802
(.046)***
0.784
(.046)***
0.792
(.047)***
0.787
(.048)***
0.816
(.048)***
Year
-0.025
(.004)***
-0.024
(.004)***
-0.025
(.004)***
-0.024
(.004)***
-0.025
(.004)***
Intercept
48.143
(8.037)***
47.260
(8.054)***
49.580
(8.055)***
-28.558
(14.995)*
49.001
(8.054)***
4,517
4,517
4,517
4,517
4,517
Number of Groups
284
284
284
284
284
Fixed Effects
Firm
Firm
Firm
Firm
Firm
-15,570.35
-15,582.72
-15,572.01
-15,577.30
-15,562.97
2,844.25
0.0000
2,784.18
0.0000
2,821.05
0.0000
1,561.10
0.0000
2,879.74
0.0000
Observations
Log Likelihood
Chi^2
Prob > Chi^2
Note: Standard errors in parentheses
*,**,*** significant at 10%, 5% and 1% levels, respectively