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Tsukuba Economics Working Papers
No. 2009-001

The Effect of Collaboration Network on Inventors’
Job Match, Productivity and Tenureby
Ryo Nakajima, Ryuichi Tamura and Nobuyuki Hanaki
January 2009

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANThe Effect of Collaboration Network on Inventors’
Job Match, Productivity and Tenure.∗
Ryo Nakajima †University of Tsukuba

Ryuichi Tamura ‡University of Tsukuba

Nobuyuki Hanaki §University of Tsukuba
January 13, 2009
Abstract
It has been argued in the economic literature that job search through informal job networks improves the employer–employee match quality. This paper argues that inventors’ research collaboration networks reduce the uncertainty of firms about the match qualities of inventors prior to hiring. We estimate the effect of inventors’ collaboration networks on their productivity andmobility using the U.S. patent application database. It is found that networkrecruited inventors are more productive and have longer tenure than publicly
recruited inventors. The evidence from fixed-effect regressions shows thatthe higher productivity and longer tenure of network-recruited inventors are
not solely attributable to their unobserved ability. These results are consistent with the job match hypothesis between inventors and firms through theircollaboration networks.

Keywords: job networks, match quality, inventor, mobility, productivity, patent.
JEL Classification: J44, J63, O32.
∗

The authors are thankful to Daiji Kawaguchi, Jinyoung Kim, Hidehiko Ichimura, Toshiaki
Iizuka, Hideo Owan, Dan Sasaki and seminar participants at 2008 Asian Conference on Applied
Micro-Economics/Econometrics, Aoyama Gakuin University and University of Tokyo for helpfulcomments. The project was partially financed by the Grant-in-Aid for Young Scientists of theJapanese Ministry of Education, Culture, Sports, Science and Technology; Japan Securities Scholarship Foundation and Inamori Foundation. All remaining errors are our own.
†
Doctoral Program in Economics, Graduate School of Humanities and Social Sciences, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba, Ibaraki 305-8571, Japan. E-mail: nakajima@dpipe.tsukuba.ac.jp; Tel: +81-29-853-5064; Fax: +81-29-853-7440.
‡
tamura@dpipe.tsukuba.ac.jp§
hanaki@dpipe.tsukuba.ac.jp

1

1 Introduction
It is widely accepted that the mobility of inventors is an important source of knowledge transfer among research firms (e.g., Arrow, 1962; Levin, Klevorick, Nelson,
Winter, Gilbert, and Griliches, 1987; Almeida and Kogut, 1999). Firms use inventors’ mobility to acquire external knowledge for new innovations (Rosenkopf andAlmeida, 2003; Song, Almeida, and Wu, 2003). Yet, it may not be obvious whichinventors’ firms should hire from their large potential employee pool, because firms
may face difficulty in ascertaining how well an inventor is matched with them prior
to employment. Some hired inventors may be poor matches for the job they holdand thus turn out to be not as good as they initially appeared. Because a better job
match leads to higher inventive productivity, one of the fundamental issues in theindustrial organization literature is to identify a mechanism that facilitates a good
match between inventors and firms. Hence, the following questions should be addressed. Which source of information do inventors and firms employ to improvethe match quality? How does such a mechanism influence inventors’ mobility andproductivity?
Recent developments in the literature on social and economic networks may offer a clue to the above research questions. One of the most widely documented facts
about job search is that networks of personal connections, often called “old-boy”
networks or informal job networks, can be used by employers to assess their job applicants’ motivation, ability, and likelihood of success. For example, Rees (1966)
finds that recruiting through informal job networks accounts for about 50 percent
and 80 percent of all hires in white-collar and blue-collar occupations, respectively.
Granovetter (1995), in his survey of residents in Newton, Massachusetts, in the late1960s, also finds that more than half of the respondent s jobs were obtained throughpersonal connections.
Theoretical studies of informal networks in labor markets1 investigate the implications of the prevalence of informal job networks in the labor market by focusing on such functions of networks as (i) transmission of job opening information(Calvo-Armengol and Jackson, 2004; Tassier and Menczer, 2008); (ii) screening
and signaling employees’ abilities (Saloner, 1985; Montgomery, 1991; Casella andHanaki, 2006, 2008) (that is, referrals through job networks may provide firms withinformation about unobserved workers’ abilities, allowing firms to use the information to select high-ability workers); and (iii) reducing uncertainty about employee–
employer match quality (Simon and Warner, 1992; Mortensen and Vishwanath,1994) (that is, referral through job networks may provide workers with information about unobserved match quality, allowing workers to self-select themselves to
1

See Ioannides and Loury (2004) for an extensive review of the literature.

2

firms representing a good match). This paper follows this literature and analyzes
the effect of inventors’ job networks on their mobility and productivity.
We develop a simple model of search for a good match value. In the theoretical model, we assume that an inventor and a firm match through either network
recruitment or public recruitment. We posit an inventor as network recruited if he
was employed by a firm through the reference of his collaborator (or collaborators)
with whom he had worked in past research activity. Both inventors and firms are
uncertain about their match value prior to hiring. However, the match value is lessuncertain for network-recruited inventors than for publicly recruited inventors. A
recruiting firm can infer the match value of a potential employee more precisely
if references from his past collaborators are available, and, at the same time, aninventor who uses his collaborator network can estimate more precisely how well(or how badly) matched he is for the offered position by a potential employer. The
main predictions of our model are: (1) network-recruited inventors have higher productivity, at least initially, than publicly recruited inventors because a good matchis more likely to occur; and (2) they have longer tenure because they are less likely
to be disappointed with their revealed match value and thus are less likely to quit.
This paper examines the predictions of the theoretical model by making use of
the US patent application database provided by the National Bureau of Economic
Research (NBER). We recompile the patent data by each inventor. Because thename of the patent assignee, which is typically the inventor’s employer, is listed ineach patent application, we can track down the companies by which each inventor
had been employed over time and thus can identify the inventors’ employment histories. In the process of tracing inventors’ mobility, identification error, often called
the “Who is Who” problem (Trajtenberg, Shiff, and Melamed, 2006) because of thepossibility of multiple name spellings for the same person and the possibility of thesame name for different persons, cannot be avoided . To minimize the error, wedeliberately use a computer matching procedure that has been recently proposedby Trajtenberg, Shiff, and Melamed (2006).2
The main findings of this paper are as follows. Comparing employment durations, network-recruited inventors have significantly longer tenure than publicly
recruited inventors. As for inventors’ productivity, which is measured by the number of successful patent applications made in a year, network-recruited inventors
are substantially more productive than publicly recruited inventors. It is also foundthat after-job-switch productivity is substantially higher for the network-recruited
inventors who are referred by high-productive referees than for those who are re2

Recently, several papers (Agrawal, Covkburn, and McHale, 2006; Schankerman, Shalem, andTrajtenberg, 2006; Marx, Strumsky, and Fleming, 2007; Hoisl, 2007) employ a similar identification
method.

3

ferred by low-productive referees, suggesting that the collaboration network acts
as a screening mechanism to identify inventors’ research ability. Nonetheless, evenafter controlling for positive association between the recruited inventor’s productivity and their referee’s productivity, a substantial productivity advantage of networkrecruited inventors over publicly recruited inventors remains. Furthermore, the initial productivity advantage of network-recruited over publicly recruited inventors
declines as tenure progresses. All these findings are consistent with the job match
hypothesis that the job network reduces ex ante uncertainty about match value between inventors and firms. Finally, the evidence from the fixed-effect regressionthat controls for individual heterogeneity suggests that the observed productivity
difference between network-recruited and publicly recruited inventors is not only
a result of network-recruited workers having higher unobserved abilities than publicly recruited inventors.
This paper is related to two strands of literature. First, it is related to the empirical labor literature that estimates the effects of informal job referral on workers’
tenure and wage profiles. Many studies find that workers who use references have
longer employment tenure than those who do not use references (Loury, 1983;
Topel and Ward, 1992; Simon and Warner, 1992; Loury, 2006). In contrast, theresults are mixed for the effect of job references on workers’ wage profiles. Somestudies present evidence that workers with a referral have higher wage premiums, atleast initially, than workers without a referral (Simon and Warner, 1992; Marmarosand Sacerdote, 2002). In contrast, other studies conclude that higher wages are
not necessarily associated with job references. Bridges and Villemez (1986) andMarsden and Hurlbert (1988) find no general and initial wage premium for referredworkers. Kugler (2003) finds that higher wage premiums for referred workers isonly between, not within, industrial sectors. Pellizzari (2004) finds that, using thedata of European Union countries, both wage premiums and penalties exist for referred workers across countries and industries. Antoninis (2006) studies the labormarket of the Egyptian manufacturing sector and finds that referred workers takewage penalties in the case of recruitment to low-skilled jobs; however, if workers
are referred by individuals who have direct working experience with them, they canobtain wage premiums in both low- and high-skilled jobs. Finally, Loury (2006)shows that only young males who are referred by older-generation male relatives
enjoy higher wages; however, no significant job reference effect exists for othergroups of workers. It should be noted that most of these papers study the effect of
informal job referrals for general workers, with the exception of Simon and Warner(1992), who use the 1972 Survey of Natural and Social Scientists and Engineers
and study the role of informal recruitment methods through personal references ina job search for “scientific researchers”.
Our paper departs from these empirical studies in that we study inventors who
4

actively engage in research activities, and we estimate how the existence of personal connections influences their research productivity and employment duration
after moving into a new firm. Given that our focus is on inventors, we directly estimate the effect of network references on productivity, rather than wages. We also
refine the definition of a job reference network. In the previous studies, job references through friends, family, acquaintances and relatives are mainly considered
to convey the job match information. In contrast, we use inventors’ research collaboration networks as a channel for job information flow. Given that the growing
importance of teams in research is one of the major trends in science nowadays,
we hypothesize that past research collaboration provides rich information for bothinventors and R&D-intensive firms to judge the job potential of an inventor in a
research position.
The second strand of the literature to which this paper relates is the empiricalindustrial organization literature, which studies the extent of the mobility of inventors and its implications for innovation. Kim and Marschke (2005) analyze therole of patenting for firms to protect their inventive knowledge against spillovers
through labor mobility. They find that firms’ patenting and inventors’ mobility
are positively correlated. Hoisl (2007) studies the mobility of inventors by using
German patent application data and finds that there are simultaneous relationships
between inventors’ mobility and productivity. It is shown that inventors with higher
productivity are less likely to move, and at the same time, movers are more productive than nonmovers. Schankerman, Shalem, and Trajtenberg (2006) study inventors’ mobility in the US software industry and find no evidence that the qualityof patents increases after their job changes. This suggests that inventor mobility does not necessarily improve the match quality between inventors and firms.
While these studies focus on the relationship between inventor mobility and productivity, this paper, in contrast, studies the effects of the job search method on
the mobility and productivity of inventors. According to our hypothesis, matchvalue is improved when firms and inventors meet through a third-party reference.
Therefore, inventor mobility alone may not necessarily lead to improvement of theemployer–employee match value.
The rest of this paper is organized as follows. Section 2 develops a theoreticaljob match model of inventors and provides the hypotheses to be tested. Section
3 describes the dataset we used for estimation. Section 4 explains our empiricalstrategies and presents the estimation results. Section 5 concludes.

5

2 Matching Model
2.1 Behavioral Model
Following the study of Simon and Warner (1992), we use the simple job matchingmodel by Jovanovic (1984). The simplicity of this model allows us to obtain anumber of comparative static results, which are later examined in the empiricalanalysis.
Consider a situation where an inventor is searching for a job. We assume thatthe ith inventor and the jth firm are matched randomly. The ith inventor maximizes
his expected sum of discounted wages given by:U=∞!

β t wijt ,

(1)

t=0

where β is the discount factor, and wijt is inventor i’s wage at firm j in period t.
We assume that inventor i’s match productivity at firm j is given by θij , whichis unknown to both the inventor and the firm in period t = 0. They can observe
the noise-ridden version of the true match value, which is given by θij + εi , where
εi is white noise. It is also assumed that they can observe the true match value θij
after the inventor works at the firm for one period. The firm chooses the pay of
inventor i to maximize the expected profit subject to a constraint of zero expected
profit. It is shown by Jovanovic (1984) that one of the best strategies of the firm is
to pay to the inventor the expected productivity of the match value, given the firm’s
error-ridden prediction of it—that is, qij = E(θij |θij +εi ) in the period t = 1—andto pay the actual productivity θij in the period t ≥ 2. Therefore, the wage profile
paid by the firm is:"
qij if t = 1
wijt =(2)
θij if t ≥ 2.
In what follows, the subscripts i and j are suppressed for notational simplicity.
The probability structure of the job matching process is specified as follows.
Let θ be a match value. The value is assumed to be firm specific. That is, whenever
the inventor applies to a firm, a new productivity θ is drawn independently froman identical normal distribution with mean µ and variance σθ ; that is, N (µ, σθ2 ).
However, once it is drawn, the value does not change over time while at the firm.
As noted above, θ is unobservable, and the firm estimates θ using its noisy signal(θ + ε). The estimate is paid to the inventor as an entrance wage. We assume thatthe noise ε is independently and identically distributed (i.i.d.) following N (0, σε2 ).
Because of the normality assumptions of θ and ε, the posterior distribution of the6estimate on the productivity θ is also normally distributed with mean q and variances2 . Bayes’ law implies the following well-known equalities.
#
$
#
$
1
1 −1
µ
θ+ε
2
s2 =
+
,
q
=
s
+
σε2
σθ2 σε2σθ2Thus, the p.d.f. of the posterior distribution of θ is given by f (θ|q, s2 ) = φ ((θ − q)/s)
and the c.d.f. is given by F (θ|q, s2 ) = Φ ((θ − q)/s). Note that the estimate q isa random variable itself, and it follows a normal distribution with mean µ andvariance σq2 = σθ4 /(σε2 + σθ2 ). The p.d.f. and c.d.f. of the belief q are given byg(q|µ, σq2 ) = φ ((q − µ)/σq ) and G(q|µ, σq2 ) = Φ ((q − µ)/σq ), respectively. The
true match value θ is assumed to be perfectly revealed both to the inventor and to
the firm at the beginning of period t = 2.
Given the wage schedule, equation (2), the inventor maximizes the expected
sum of discount wages, which is given by equation (1). We assume that, because
the constant match value is paid to the worker as a wage after period t = 2, nodecision occurs after the second period. The decision problem can be solved backwardly. Suppose that the inventor is employed in period t = 1. At the beginningof period t = 2, the inventor decides to continue or quit the job when the firm
offers him the match value θ. Let J(θ) be the present value of staying in the job.
The present value of accepting the offer θ is given by θ + βJ(θ), where β is thediscount factor. If he rejects it, he receives nothing, and becomes unemployed in
the next period. Let W be the present value of being unemployed at the beginningof a period. Because of the assumption that the match value is invariant once it isrealized, we have the following recursive equation for J(θ), such that:
J(θ) = max {θ + βJ(θ), βW } .

(3)The decision of staying or leaving the job at the end of period t = 1 is characterized by a reservation value, θ∗ , below which the inventor leaves the job. J(θ) is,
therefore:"θ
if θ ≥ θ∗J(θ) = 1−β,(4)βW if θ < θ ∗where the reservation value is given by:
βθ∗
= βW
1−β
θ∗ = β(1 − β)W.
θ∗ +
⇒

(5)

Given the inventor’s decision in period t = 2, we now turn to the decision
problem at the beginning of t = 1. Suppose that the inventor is offered an entrance
7

wage q by the firm. If he accepts the job offer, his expected present value of the
future wage flow is given by q + β E[J(θ)]. Because the posterior distribution of θ
is N (q, s2 ), the expected value of the decision to stay at the job is computed by:%
E[J(θ)] =J(θ)dF (θ|q, s2 )%=J(θ)dΦ ((θ − q)/s) .
(6)
Therefore, the present value of accepting the offer q is given by:
V (q) = max {q + β E[J(θ)], βW }&'%2
= max q + β J(θ)dF (θ|q, s ), βW .

(7)

The value function V (q) is monotonically increasing in q, and thus the decision
whether to accept the job offer is characterized by the reservation wage q ∗ thatsatisfies:%∗
q + β J(θ)dF (θ|q ∗ , s2 ) = βW.
(8)Given that J(θ) is determined by equation (4), we have the following relationship.
%β∗∗q =θ −(θ − θ∗ )dF (θ|q ∗ , s2 )(9)
1 − β θ∗Because the second term of the right-hand side of equation (9) is positive, θ∗ >q ∗ . That is, the reservation value in the second period is always larger than thereservation wage in the first period.

2.2 Role of Collaboration Networks
Suppose that when a firm recruits inventors, its current employees can recommend
someone with whom they have previously collaborated. Of course, the firm canhire an inventor without a referral. We categorize inventors into two groups basedon whether they are recruited with or without a referral from their collaborators.
Let us call those inventors recruited with a referral network recruited (k = N ) andthose without publicly recruited (k = P ).
2 be the error variance to the productivities of inventors in group k . We
Let σεkassume that referrals based on past collaboration are informative so that firms canpredict the productivity of a network-recruited inventor more precisely than that of8

2 < σ 2 . For inventors in group k, let q bea publicly recruited inventor; i.e., σεNkεP
the entrance wage, and let θk be the match value.
We first analyze how employment duration is affected by the recruiting method.
Recall that the firm and inventor can perfectly observe the true match value θ atthe beginning of the second period. The inventor leaves the job if it is less than
the reservation value θ∗ . Therefore, the probability that an inventor leaves theemployed firm is given by:
# ∗
$
% θ∗
θ −µ
2
dF (θ|qk , sk ) = Φ.
sk
2 . Because
It can be easily shown that this probability is an increasing function of σεk22σεN < σεP , publicly recruited inventors are more likely to leave the employed firm
than network-recruited inventors. This observation leads to the following proposition about employment duration.

Proposition 1 The network-recruited inventors will have longer employment duration than the publicly recruited inventors.
Logically, a network-recruited inventor is less likely to quit his job than a publicly
recruited inventor because, employed through a collaboration network, he is more
likely to have a “good match” and is thus less likely to be disappointed with hismatch value with the firm. Therefore, we have the above proposition.
We now turn to the effect of referrals on the average productivity in the first period. For simplicity, we assume that firms employ referred and nonreferred inventors with probability p and 1 − p, respectively.3 Then, the value of unemployment,
W , is given by:W= p E[V (qN )] + (1 − p) E[V (qP )],%%22= p V (qN )dG(qN |µ, σqN ) + (1 − p) V (qP )dG(qP |µ, σqP),

2 = σ 4 /(σ 2 + σ 2 ) for k = N, P . It is important to note that the reserwhere σqk
θεkθ
vation value θ∗ does not depend on the existence of referrals because it is given by3
We do not explicitly model which firm uses employees’ referral in hiring an inventor because
it is beyond the focus of the current paper. However, the existing theoretical works, (e.g., Saloner,
1985; Montgomery, 1991) suggest that firms with productive employees will hire through referral,
while those without such employees, do not. It is also possible that the current employees of the firm
do not have useful connections with others (Casella and Hanaki, 2008). Because we do not assume
a productivity difference among workers, the probability p with which a firm hires a referral worker
can be interpreted as the probability at which the current employees of the firm have meaningfulconnections with other inventors.

9equation (5). On the other hand, the reservation wage q ∗ depends on whether theinventor is network recruited or publicly recruited. According to equation (9), thereservation wages are provided by:%β∗∗
qk = θ −(θ − θ∗ )dF (θ|qk∗ , s2k ),1 − β θ∗where s2k (is the variance of the posterior distribution of θ for type k inventors.
Note that θ∗ (θ − θ∗ )dF (θ|q ∗ , s2 ) is increasing in s2 = (1/σε2 + 1/σθ2 )−1 , and2 < σ 2 implies that
thus increasing in σε2 . The order of prediction precision σεNεP∗ > q ∗ . It thus implies that the reservation wages of network-recruited inventors
qN
P
are higher than those of publicly recruited inventors.
Given the entry wage q offered by a firm, a type k inventor accepts the offer,and is employed by the firm if it is above the reservation wage q > qk∗ for k = N, P .
Therefore, the mean productivities for a type k inventor are given by:*( )(θ q∗ φ ((θ − q)/sk ) φ ((q − µ)/σqk ) dq dθk+,E(θ|q > qk∗ ) =.
Φ (µ − qk∗ )/σqk
The numerator can be computed as:.
% -%θφ ((θ − q)/s) dθ φ ((q − µ)/σqk ) dq.
qk∗

Because q =(θφ ((θ − q)/s) dθ, we obtain the following result.
(q ∗ qφ ((q − µ)/σqk ) dq,
E(θ|q > qk∗ ) = k += E(q|q > qk∗ )Φ (µ − qk∗ )/σqk(10)

It can be easily seen that E(q|q > qk∗ ) is an increasing function of qk∗ , and so is∗ > q ∗ . It is thusE(θ|q > qk∗ ), given the result (10). As shown above, we have qNP
implied that:∗E(θ|q > qN
) > E(θ|q > qP∗ ).

This result implies the following proposition.
Proposition 2 Network-recruited inventors exhibit a higher initial productivity thanpublicly recruited inventors, on average.
The intuitive reason for network-recruited inventors tending to show higher productivity than publicly recruited inventors is as follows. If the firm is more certain
10

about a recruited person’s match value, a mismatch is less likely to occur. Thus,
the referrals allow the firm to select more inventors having a “good match”, and,at the same time, allow more inventors to self-select themselves into the jobs inwhich they are more productive.
We turn to the mean productivity of inventors in the second period. The behavioral model implies that the inventors whose match values are less than θ∗have left the firm. Therefore, the productivity in the second period is given byE(θ|q > qk∗ , θ > θ∗ ). Note that the conditional density function of θ, given theevent that (q > qk∗ ) ∩ (θ > θ∗ ) occurs, is provided by:RR22
q ∗ φ((θ−q)/sk )φ((q−µ)/σqk )dq qk∗ f (θ|q,sk )g(q|µ,σqk )dq
k=if θ ≥ θ∗∗
Prob[(q>qk )∩(θ>θ∗ )]Prob[(q>qk∗ )∩(θ>θ ∗ )]
(11)

∗
0
if θ < θ .
We show in the appendix that the mean productivity in the second period is givenby:(q ∗ [q + sλ ((q − µ)/sk )] φ ((q − µ)/σqk ) dq∗∗
E(θ|q > qk , θ > θ ) = k (,(12)
q ∗ Φ ((µ − q)/sk ) φ ((q − µ)/σqk ) dqkwhere λ(t) ≡ φ(t)/(1 − Φ(t)) is the inverse Mill’s ratio. Recall that the mean
productivity in the first period is given by equation (10). Comparing equation (10)with equation (12), and because sλ ((q − µ)/σqk ) ≥ 0 for any q, we can say that:E(θ|q > qk∗ ) ≤ E(θ|q > qk∗ , θ > θ ∗ ).
We therefore have the following proposition.
Proposition 3 The average productivity weakly increases with tenure.
It should be noted that, because our model assumes no human capital accumulation,
the average productivity grows with tenure to the extent that inventors having lower
match values leave their firm as time passes. Following Simon and Warner (1992),
2
we consider the use of referrals in terms of the limiting case where σεN→ 0,2
while σεP is strictly positive for the publicly recruited inventors. Noting that s2N =2 )−1 , σ 2 → 0 implies s2 → 0. Furthermore, σ 2 → 0 implies(1/σθ2 + 1/σεN
εN
N
εN
2
σqN → 0, and thus Φ ((q − µ)/σqN ) → 1. Therefore, according to equation (12),we can see that, for the network-recruited inventors:∗∗
E(θ|q > qN
) = E(θ|q > qN
, θ > θ ∗ ).

This implies the following proposition.
11

Proposition 4 Publicly recruited inventors have higher productivity growth than
network-recruited inventors as tenure increases.
The intuitive reason why the average productivity growth is higher for publicly recruited inventors than for network-recruited inventors is that the former are more
likely to be “mismatched” , and many of them switch firms, sooner or later, once
their true match value is revealed. Thus, the average productivity of publicly recruited inventors increases with tenure. On the other hand, network-recruited inventors who have good match values are less likely to switch firms. Therefore theirproductivity is less susceptible to change.

3 DataWe base our analysis on the NBER Patent Data File.4 This dataset covers all thepatent applications between 1963 to 1999 and granted up to December 1999. For
each patent, the list of inventors, assignee, and year of application are recorded,along with other information, such as addresses of inventors, the type of assignee,
and the technological category of the patent. We supplement the month of thepatent application with the USPTO PatentBIB database. The NBER Patent Data
File contains the patent citations for patents that were applied for after 1975. Because the citation information is required to identify unique inventors, as described
below, the patents that were applied for before 1974 were excluded from our sample. It should be noted that many patents that were applied for in 1998 and 1999
are missing from the database because there is often a lag of a few years before
patents are granted.5 Therefore, we use the patents that were applied for between
1975 (the first year in which citation information is available) and 1997 (the latest
year in which the effect of truncation is not substantial) in our analysis.

3.1 Employment History
To analyze the mobility of inventors, we are required to identify, for each inventor in the dataset, his affiliation over time from the information contained in thepatents. This, however, is not a simple task because the same inventor may havehis name spelled differently across his patents, or different inventors may have thesame name. To overcome this difficulty, we follow the computerized matching procedure (CMP) proposed by Trajtenberg, Shiff, and Melamed (2006) in identifying
inventors. In doing so, CMP utilizes not only the name of inventors recorded in4

For detailed information, see Hall, Jaffe, and Trajtenberg (2001).
For example, Hall, Jaffe, and Trajtenberg (2001) show that the average time lag between theapplication and grant date in the late 1990s was 1.8 years.
512

the patents, but also patent citations, and inventors’ addresses, while allowing forthe possibility of spelling errors in names.6 In addition, to increase the accuracyof matching individual inventors, we focus on the inventors whose addresses are in
the US.
Once inventors are identified, the history of granted patents is generated foreach inventor. Furthermore, based on the application dates and assignees of thosepatents, we create his employment history. Our basic strategy is to consider the
longest possible employment durations by assuming that an inventor was employedby an assignee for all periods during which he applied for patents assigned to theassignee.
It should be noted, however, that if companies undergo a merger or acquisition, the acquired company appears under the name of the acquiring company after
the official date of merger. To avoid identifying changes in the assignee’s namebecause of M&A as changes in the inventor’s employer, we supplement our data
by SDC Platinum, the Worldwide Mergers and Acquisitions Database, issued byThomson Reuters. Among all the M&As since 1979 that are reported in SDC Platinum, we select the cases where the acquiring company obtains all the stock of thetarget company. We then consider those two companies to be in a parent–subsidiary
relationship and treat them as one company after the merger. We also subsample
the inventors whose lists of assignees are categorized as private companies locatedin the US.7
Let us now describe, in detail, how we construct the employment histories forinventors. As noted above, our basic strategy is to consider the longest possible employment durations. We list all the assignees of the patents listed in the individual
history. Then, given the listed assignees, we take the earliest and the latest patentapplication dates, and consider the interval between the two dates as a candidate’s
job spell (CJS).8
After identifying all the CJSs and sorting them based on their starting date,(1) we first eliminate all the CJSs that are contained entirely within a longer CJS.
We assume that those patents that have created such shorter CJSs are the resultof interassignee collaborations, and the inventor continued to be employed by theoriginal employer during such collaborations.9 (2) Among the remaining CJSs,
6

The details of this procedure are summarized in the appendix.
To identify the type of assignees listed in the patent application data we utilize the corporate andnoncorporate name-matching results available from Bronwyn Hall’s web page of The Patent NameMatching Project (http://elsa.berkeley.edu/∼bhhall/pat/namematch.html). In
this analysis, we exclude assignees categorized as government institutions, universities and hospitals.
8
We allow CJSs whose spell length is zero, which happens when an inventor applied for all thepatents from an assignee within a month.
9
A similar assumption is made by previous studies (e.g., Hoisl, 2007).
7

13

we drop CJSs that overlap with each other. We do this because we are unable todetermine when the inventor moved from one assignee to another. This criterion
is quite stringent, and creates many empty, and often long, intervals in inventors’
employment histories. Yet we have chosen to follow this rule because our aim is
not to have a complete employment history of all the inventors, but rather to analyze the job tenures and productivities of inventors for the periods of employmentthat are defined, as clearly as possible. We consider the CJSs that have survived
these two elimination processes as valid job spells (to be called job spells, below).
Furthermore, during each job spell, we assume that the inventor was employed by
the corresponding assignee.
The procedure is summarized in Figure 1. The inventor shown in the figure
applied for 11 patents under five different assignees. Given that Pij indicates theapplication date of the j-th patent that this inventor applied for under assignee i,the figure shows that we have five CJSs. Because CJS3 is contained in CJS2 , it isdropped. Furthermore, because CJS4 and CJS5 overlap with each other, they are
dropped as well. As a result, CJS1 and CJS2 , shown in bold and with arrows, areconsidered as job spells, during which the inventor obtained three and four patents,
respectively.
Given the data construction procedure presented above, we find 51,896 inventors who experienced at least one job change. For those inventors, 115,307 job
spells are identified.10 It is found that more than 95 percent of those inventors only
had either two or three job spells.

3.2 Recruitment Method
We can obtain information concerning the recruiting method, whether network recruited or publicly recruited, from the patent collaboration histories of colisted
inventors. In doing so, we define the set of collaborators of inventor i on date t
by all the inventors who are colisted in the patents investor i has applied for (andbeen later granted) before date t. For example, for the inventor shown in Figure 1,
the set of collaborators on date P21 constitutes all of the coinventors listed in thethree patents applied for during spell 1 (those applied for on P11 , P12 , and P13 ).
Given the set of his collaborators, we identify collaborator i as network recruited
if at least one of his collaborators is employed by the same firm at the beginning of
inventor i’s job spell. If this is not the case, inventor i is considered to be recruited
publicly.
It is possible that more recently established collaborations generate more meaningful referral. Therefore, in the analyses below, we also consider more restricted
10

We find that the total number of CJSs is 118,447. This implies that the identified job spells(115,307) account for 97 percent of the total CJSs.

14

sets of collaborators in defining network recruitment; namely, in addition to theset of collaborators defined above (we call these “overall collaborations”), we also
consider sets of collaborators at time t based on the successful patents that havebeen applied for within 12, 24, and 36 months prior to date t.
Using the definition of the overall collaboration network that encompasses all
the past collaborations, we find that 10,758 jobs are originated via network recruitment, which corresponds to 9.33 percent of total jobs. If the collaboration network
is restricted to 12, 24 and 36 months, the network-recruited jobs are 2,538 (2.20
percent), 5,081 (4.41 percent) and 6,794 (5.89 percent) respectively.

4 Empirical Results
In this section, we first examine the prediction presented by our theoretical modelthat employment durations are different between network-recruited inventors andpublicly recruited inventors. We then examine the predictions of the model that
productivities are also different between these two groups of inventors.

4.1 Employment Duration Results
Network-recruited inventors, with relatively better match values, should be lesslikely to leave firms than publicly recruited inventors, as presented by Proposition 1. We test this hypothesis using employment duration data. Because no inventors are recruited via collaboration networks for the first jobs, we examine thishypothesis only for subsequent jobs of inventors who switched their jobs at least
once. Therefore, the first job spells are excluded from the estimation samples.
Figure 2 plots the survival curves for employment duration after job transition
for network-recruited inventors and publicly recruited inventors, respectively. We
follow the definition of network recruitment as presented in the previous section.
Because the job spell data are arbitrary censored,11 we employ a nonparametricmaximum likelihood estimator for interval-censored data proposed by Turnbull(1976). It is shown that the employment duration is almost uniformly longer for
network-recruited inventors than for publicly recruited inventors.
11

Our job spell data are arbitrarily censored because the value is known to lie in an interval, insteadof being observed exactly. To understand this, for example, consider a researcher who changed
firms twice, say, firstly from firm f1 to firm f2 , and secondly from firm f2 to firm f3 . Given theemployment histories constructed by the patent file, we know that he entered firm f2 sometime after
the calendar month, r1 , at which time he applied for a patent at firm f1 for the last time, and sometime
before l2 , at which time he applied for a patent at firm f2 for the first time. Similarly, we know thathe stayed at firm f2 at least until r2 , at which time he applied for a patent at firm f2 for the last time,and not after l3 , at which time he applied for a patent at firm f3 for the first time. In that case, the job
spell at firm f2 , denoted by t2 , is interval-censored data with (l2 − r2 ) < t2 < (l3 − r1 ).

15

Table 1 contrasts the median employment durations between publicly recruited
inventors and network- recruited inventors for the four collaboration networks wehave considered. It shows that the median employment duration is always longer
for network-recruited inventors than for publicly recruited inventors. We use thelog-rank statistic (Peto and Peto, 1972) to test the equality of the survival functions
between these two groups, and find that all of these tests are strongly rejected
at the 1 percent significance level. We find that median employment duration is
71 months for publicly recruited inventors and 93 months for network-recruited
inventors. That is, it takes about six years (eight years) for half of publicly recruited(network-recruited) inventors, respectively, to leave firms.
We also estimate hazard regression models of inventors’ job turnover, controlling for their characteristics. The hazard function is given by a Weibull specification with h(tif |Xif ) = exp(Xif β + δN ETif )αtα−1
if , where tif is inventor i’s
employment duration at firm f , and Xif is a vector of time-invariant individual
characteristics. We explicitly control the network-recruited dummy, N ETif whichtakes a value of one if inventor i is a network-recruited inventor at firm f , and zerootherwise. According to Proposition 1, we expect that δ < 0; that is, networkrecruited inventors are less likely to leave the employed job than publicly recruited
inventors. As other explanatory variables, Xif , we include the years of researchexperience before being employed by firm f . This is given by the number of years
since the inventor i applied for patents for the first time until the time when he isemployed by firm f , which is called research experience in the regression. We also
include research field dummies. The research field dummy is defined for the six
main technological categories (m = 6): chemical (excluding drugs), computersand communications, drugs and medical, electrical and electronics, mechanical,
and others.12 The mth field dummy takes a value of one if inventor i has applied
for a patent in the mth field at firm f in year t. Finally, we include the total number of patents and the annual average number of patents that inventor i applied for
before joining firm f , which are called total and average past patent productivities
in the regression. They are used as proxies for the inventor’s research ability.
Table 2 presents the estimation results of the hazard regression model. As inthe survival curve analysis, interval censoring is taken into account for estimation.
We use the total number of previously applied patents in column (1) and use theaverage number of previously applied patents in column (2) to control for the inventor’s ability, respectively. In both specifications, the coefficient δ is negativeand statistically significant, and thus the network recruitment method significantly
decreases the hazard of leaving the employment. Therefore, the network-recruited
inventors are likely to stay longer at the firm than the publicly recruited inventors.
12

We follow the technological category definitions of Hall, Jaffe, and Trajtenberg (2001).

16

In columns (3)–(5), we use various network- recruitment measures that limit theintervals after the collaborations were made. The estimates of the coefficients of
network-recruited dummies are significantly negative in all specifications. Theseestimates imply that network-recruited inventors are about 40 percent less likely toleave the job than publicly recruited inventors.

4.2 Productivity Results
Our theoretical model presents two main empirically testable hypotheses about inventors’ productivity. The first hypothesis, given by Proposition 2, is that networkrecruited inventors have a higher initial productivity than publicly recruited inventors, on average. The second hypothesis, given by Proposition 4, is that within-firm
productivity growth rates are different between publicly recruited inventors andnetwork-recruited inventors; in particular, the former has a steeper productivitytenure profile than the latter. Thus, the initial productivity advantage of networkrecruited inventors over publicly recruited inventors disappears as tenure progresses.
To examine these hypotheses, we introduce a regression framework. The dependent variable of the regression is the number of successful patent applications madeby an inventor in one year, which is considered to be a measure of the inventor’s
productivity. Because the dependent variable is an integer variable with many zeros
and ones, we use a Poisson-based specification as in Hausman, Hall, and Griliches(1984) and Hall and Ziedonis (2001). Consider an inventor i who works at firm f .
We assume that the expected number of patents, Pif t , applied for by the inventor
in year t, conditional on the characteristics of the inventor and firm, is given by:E(Pif t |Xif t , N ETif ) = exp(α + Xif t β + ρN ETif ),(13)

where Xif t is a vector of individual-firm time-specific variables.
A key variable in our specification above is the network-recruited dummy,
N ETif , which takes a value of one if inventor i is a network-recruited inventor
at firm f , and zero otherwise. The scalar coefficient ρ of the network-recruited
dummy can be interpreted as the match premium for network-recruited inventors
over publicly recruited inventors. According to Proposition 2, the network- recruited inventors can obtain the information about their match values with theirpotential employers through their collaboration network, and thus have better initial match values than publicly recruited inventors. We thus expect that the matchpremium is positive in the early stage of tenure.
As presented in the introduction, however, the role of collaboration networks
may not be confined to reducing match uncertainty between inventors and firms.
It is argued by previous studies that network references may act as a screening
device in the selection of high-ability workers. Knowing that referees tend to refer17others who are similar to themselves, firms have incentives to select inventors who
were referred by high-productivity referees rather than those who were referred
by low-productivity referees. Therefore, if the collaboration network works as ascreening device, the productivity of a network-recruited inventor will be positively
correlated with the productivity of the referee who referred the inventor to the firm.
To capture the screening effect through the collaboration network, the regression variables, Xif t , include the productivity of the referee inventor who referred
inventor i to firm f (we call this the referee’s productivity). For network-recruited
inventor i, we define his referee as one of the past collaborators who was employedby firm f in year t. We measure the referee’s productivity by the total numberof patents that he had applied for at firm f before inventor i switched to the firm.
For publicly recruited inventor i, because he was not referred by anybody at thejob switch, the referee’s productivity variable is set to be zero. If the screening
mechanism works for the collaboration network, it is predicted that the referee’s
productivity will be positively related to network-recruited inventor i’s productivity, and thus the sign of the estimated coefficient of the referee’s productivity will
be positive. It should be noted that the screening effect is not captured by the matchpremium ρ of the coefficient of N ETif . The job match hypothesis implies that thematch premium will be positive ρ > 0 no matter who is the referee. In other words,
a network-recruited inventor is able to obtain information about his match value
with the potential employer even though the referee exhibits low patent productivity. Therefore, the job match hypothesis alone cannot explain positive associationin productivity between the referee inventors and referred inventors.
As other variables for Xif t , we include the years of potential research experience since inventor i applied for patents for the first time (we call this the total
research experience), and the years of tenure at firm f (we call this the withinfirm research experience). Both years of experience are measured as of period t.
While the years of total research experience are accumulated over time, the yearsof within-firm experience are reset to be zero whenever the inventor switched hisjobs. We also include research field dummies for m = 6 categories, and the total
number of patents that the inventor i has applied for before firm f (we call this thepast patent productivity) in the regression.13 As in the hazard regression, it is used
as a proxy for the inventor’s innate research ability. In addition, we include the total
number of patents that were applied for by all inventors employed at firm f during
year t (we call this the firm’s patent productivity). This variable is considered to
be a proxy for the research capacity of the firm. It is expected that the higher the13

Given that the average number of patents is more susceptible to interval censoring of employment duration than the total number of patents, we use the total number of previously applied patents
as a proxy for inventors’ ability in what follows.

18firm’s research capacity, the more patents the firm produces, and vice versa.14 Finally, following the previous literature (e.g., Hall and Ziedonis, 2001), we include
annual dummies, which account for the growth of patenting propensities.
We estimate the Poisson regression model (13) presented above using the dataset
described in Section 3. We again restrict samples to the inventors who experienced
at least one job transition because no inventor is recruited via his collaboration
network for his first job. It thus implies that we estimate the effect of referrals oninventors’ productivity after switching firms.15 As should be clear from the above
discussion, the data have an unbalanced panel structure with individual-firm-year
being the unit of analysis. The data include 286,955 units of observation for 51,896
inventors. Table 3 shows the summary statistics of the patent counts and main explanatory variables used in the regression analysis.
Table 4 presents the estimation results of the baseline model.16 In addition to
the variables that are explained above, we included the first-year tenure dummythat takes a value of one for an inventor who was in the first year of employment.
The variable is included to control for our job spell construction property that atleast one patent is included in the first tenure year. We report the heteroskedasticity
robust standard errors in parentheses. They are known to be consistent even under
misspecification of the distributional assumption.17
Column (1) shows the estimation result using the network-recruited dummy
constructed from the overall collaboration network. It is found that the estimatesof match premium of the network-recruited inventors, ρ, is positive and statisticallysignificant. Columns (2)–(4) present the estimation results in which collaborations
only from limited intervals before the job switch are considered. It is also foundconsistently that the estimated values of ρ are positive and statistically significant.
All these findings confirm that network-recruited inventors are more productive
than publicly recruited inventors. More interestingly the match premium increases
as the coverage period of collaboration networks becomes shorter. It suggests that
better job matches are more likely to occur between inventors and firms if referrals
are based on more recent collaborations. These findings are consistent with ourview that the collaboration network is a method by which agents obtain information
about unobserved match quality, and the more recent the information, the more
14
Often the research capacity of a firm is measured by its R&D expenditure. We do not use it here
because in our sample there are firms that are not listed on the stock market and such data are notavailable.
15
The effect of network job referrals on the first employment productivity of workers, although
they are not necessarily inventors, is analyzed by Simon and Warner (1992) and Loury (2006).
16
In this and the following tables, the estimates of the annual dummies and research field dummies
are not reported. All the estimation results are reported in the appendix.
17
See ? for a detailed discussion.

19

certain they are about the quality of their match.
The estimation results also show that the coefficients of referees’ productivity
are positive and statistically significant, predicting that inventors who were referredby higher-productivity referees tend to be more productive than those who were referred by lower-productivity referees. This suggests that firms may use the referee’s
productivity to select high-ability inventors among potential employees. Nevertheless, the fact that a substantial match premium remains after controlling for the
network screening effect seems to support the job match hypothesis. These findings thus indicate that the collaboration network has two roles at the same time:
one is to provide firms with the information to screen inventor’s ability, and theother is to provide firms and inventors with information about their match quality.
As for the other control variables, many of the estimates confirm our priorexpectation. As one expects, the coefficients of the past patent productivity are
positive and statistically significant, suggesting that inventors who had applied formore patents tend to produce more patents after their job changes. Furthermore,
the coefficients of the firm’s patent productivity are, as expected, positive and statistically significant. Though somewhat puzzling, the change in productivity isfound to follow a convex relationship with the total and within-firm research experiences. We recognize that these research experience variables might be correlatedwith the unobserved ability of the inventor, and thus may be endogenous18 . Therefore, these results about research experiences should be taken with caution. In thenext section, we consider more seriously the problem of the individual unobserved
factors that make some explanatory variables endogenous.
We now turn to the second hypothesis that within-firm productivity growth isdifferent between network-recruited and publicly recruited inventors. In order toexamine this hypothesis, we add the interaction terms of the network-recruited
dummy and tenure dummies to the baseline regression model (13). The regression
model is then given by:E(Pif t |Xif t , N ETif ) =exp(α + Xif t β +

K
!
k=1

ρk (N ETif · T enureif tk ),(14)

where T enureif tk is the kth tenure year dummy for inventor i at firm f , and takes
a value of one if inventor i is employed in the kth year by firm f . In this withinfirm productivity growth regression, the coefficient ρk of the interaction term cap18
For example, a person with a higher ability will stay being an active patent inventor longer, sothat his research experience will increase. Even though we include the numbers of the previously
applied patents to control for the inventors’ ability, we may not be able to exclude the possibility that
there is still unobserved individual ability left.

20tures the productivity premium for network-recruited inventors over publicly recruited inventors observed in the kth tenure year. A positive value of ρk means that
network-recruited inventors have a higher productivity
than publicly recruited in2
ventors in tenure year k. As can be shown easily, k ρk = ρ. Although the theoretical model considered above assumes that agents learn about the true match value
after one period, such learning may take place over several years in reality. Therefore, given the hypothesis that within-firm productivity growth is faster for publicly
recruited inventors than for network-recruited inventors, we predict that the coefficient ρk is weakly decreasing with tenure year k; i.e., ρ1 ≥ ρ2 ≥ · · · ≥ ρK .
Columns (5)–(8) in Table 4 present the estimation results for the within-firm
productivity growth regression model (14). The set of controls other than the
network-recruited dummy are the same as before. Because of the space limitation, only the estimates of the coefficient ρk s up to 10 years are presented. The fullestimation results are presented in the appendix.
The estimates overall deliver similar quantitative results as the productivity regression reported in columns (1)–(4). The coefficient ρk of the interaction terms
are positive and statistically significant for the first six to eight tenure years in all
specifications. Few estimates are found to be negative and significant for tenure
years greater than nine; however, no positive significant estimates are found. It
is also apparent that the magnitude tends to decrease as tenure year k increases,
though the declining pattern is not necessarily uniform. This means that the initial productivity advantage of network-recruited inventors over publicly recruited
inventors diminishes with tenure years, and disappears eventually. These findings
are consistent with the proposition that publicly recruited inventors have a steeper
tenure-productivity profile than network-recruited inventors, and thus supports thejob match hypothesis presented by our theoretical model.

4.3 Robustness Check
The estimation results presented above indicate that the positive-match premium of
network-recruited inventors is consistent with our theory that a reference throughcollaboration network may create better matches between inventors and firms.
However, alternative explanations to such an interpretation are possible. First,
higher-ability workers may receive more referral offers, and thus referrals may
be correlated with the unobserved ability of the inventor. If this is the case, theobserved productivity difference between network-recruited and publicly recruited
inventors can be explained by the unobserved difference in abilities between thesegroups. Second, the cost of patent production per inventor may decline with thenumber of coauthors per patent. Therefore, inventors who tend to work in groups
or collaborate on projects might appear to be more productive than those who do
21

not, simply because they have many coauthors and thus devote relatively less timeto produce one patent. In this sense, the patent productivity computed by the number of granted patents may be “inflated” by the coauthorship. This problem may
be serious when network-recruited inventors are more likely to work in projects
than the publicly recruited inventors. If this happens, network-recruited inventors
appear to be more productive than the publicly recruited inventors, not because of
their higher match value, but because of their sheer tendency to work in groups.
In what follows, we examine closely these two alternative hypotheses to ensurethat the estimated productivity difference between the network- recruited and publicly recruited inventors is actually driven by a higher match value, as implied by
the job match hypothesis that we maintain.
To begin with, we point out that unobserved productivity cannot explain the
difference in productivity-tenure profiles between network-recruited inventors andpublicly recruited inventors. As already discussed in Simon and Warner (1992),
it is explained consistently by the fact that publicly recruited inventors are more
likely to leave firms, because of job mismatch, than network-recruited inventors.
Thus, the previous finding on different within-firm productivity growth pattern may
support the job match hypothesis.
Furthermore, a more direct support for the job match hypothesis can be presented. Given the panel structure of our dataset, we may be able to control fortime-invariant unobserved individual factors, including inventors’ innate researchabilities, by incorporating a fixed effect in the productivity regression. For that
purpose, we augmented the baseline regression model (13) by adding an individual
time-invariant fixed effect:
E(Pif t |Xif t , N ETif ) = exp(αi + Xif t β + ρN ETif ).

(15)

The fixed effect, αi , captures inventor i’s unobserved heterogeneity that affects his
research productivity. By the same token, we incorporate the fixed-effect term αiinto the within-firm productivity growth regression model (14) .
Table 5 presents the estimation results from the fixed-effect specifications.19
Columns (1)–(4) present the estimation results for the baseline model, while columns(5)–(8) present the estimation results for the within-firm productivity growth model.
The results in columns (1)–(4) show that, for all specifications of networkrecruitment dummies, the match premiums are again positive and statistically significant. Furthermore, the match premium increases among the more recent collaborations, as was previously found in Table 4. Thus, we find evidence that thecollaboration networks bring about better matches between inventors and firms
even after controlling for unobserved heterogeneity among inventors.
19

The full estimation results are presented in the appendix.

22

From the estimated coefficients of the interaction term, ρk , reported in columns(5)–(8), it is found that the interaction terms up to the eighth tenure year are positive and statistically significant in all specifications. The estimation results showthat the match premium declines monotonically after the early tenure years, anddisappears as tenure progresses. These results confirm that network-recruited inventors are initially more productive than publicly recruited inventors; however,the productivity advantage declines with tenure, and the convergence of productivity occurs between the two groups. It thus suggests that inventors learn their matchproductivity rather quickly after moving into a new firm.
These fixed-effect estimates in Table 5 are generally comparable to those in
Table 4. However, one significant difference between the pooled and fixed-effectregressions is found in the estimated coefficient of the past patent productivity.
The estimates are negative and statistically significant under the fixed-effect specification, while they are positive and significant under the pooled regression inwhich we ignore unobserved individual heterogeneity. The change in the sign ofthe estimates might be partly explained by the fact that past patent productivity,
which is used to proxy for an inventor’s research ability, captures some part of theinventor’s individual heterogeneity, and is absorbed by the fixed-effect term. Furthermore, the effect of so-called “regression toward the mean” might explain thenegative coefficient of the past patent productivity. Once both the observed andunobserved characteristics of inventors are controlled by regressors and the fixed
effect, the numbers of patents applied in two successive job spells are thought tofollow the same probability distribution. Therefore, inventors who are far from themean productivity in the first job spell will tend to be closer to the mean productivity in the second job spell, and thus there will be a negative association between
before-job-change productivity and after-job-change productivity.
It should be also noted in the estimates presented in Table 5 that there is a significant positive relationship between patent productivity and total research experience, suggesting that the inventors with longer research experience tend to produce
more patents. Interestingly, patent productivity is also associated with within-firm
research experience, yet the relationship is found to be concave.
We now turn to checking whether the positive-match value premium of networkrecruited inventors, ρ > 0, is because of the fact that the productivity is “inflated”
by coauthorships. Assuming that the coauthors contribute equally to patent production, individual contribution by each inventor to each patent is given by the
inverse of the number of coauthors of the patent. Therefore, coauthorship-adjusted
productivity is given by the annual sum of the inventor’s patent contributions per
coauthor. To be more precise, suppose that inventor i applied for a total of Mit
patents in year t. Let Nm be the number of coauthors of the mth patent form = 1, · · · , Mit . Then the coauthorship-adjusted productivity of inventor i in
23

2 it
year t is give by Pit = M
m=1 1/Nm . Using this “weighted” productivity measure
as the dependent variable, we reestimate the regressions. Because the dependent
variable is no longer integer-valued, we can use the linear regression model withor without fixed effects.
Table 6 presents the estimation results of the fixed-effect regression.20 As presented, the estimates yield similar results to those from the Poisson regressions that
do not take into account the individual-based contribution to each patent. In particular, the estimated match premium and their cross-terms with tenure dummies
exhibit the same signs and statistical significance in all specifications of the collaboration network. Thus we can say that coauthorship does not play a big role inexplaining the observed productivity difference between network-recruited inventors and publicly recruited inventors.

5 Conclusion
This paper developed a simple model of job search and matching between inventors and firms through research collaboration networks. The model’s prediction istested using panel data of affiliations and productivities of inventors constructedfrom the NBER parent database. The empirical analysis supports the predictionof the theoretical model. It is found that network-recruited inventors who moved
to companies where their research collaborators were employed had significantly
longer tenure than publicly recruited inventors who moved to companies withwhich they had no personal connections. Moreover, the former group produced
substantially more patents than the latter group for several years after their job
switches; however, the productivity gap between the two groups declined as tenure
years progressed. These findings can be consistently explained by the matchingmechanism of the informal networks that reduce uncertainty in job matches between employers and employees. Interestingly, there is also evidence that informal
networks work as a screening mechanism, and it enables firms to extract information about the potential employee’s unobserved ability through their referee’s
observed abilities. Finally, fixed-effect regression results showed that unobserved
individual characteristics cannot explain all of the productivity differences between
network-recruited inventors and publicly recruited inventors.
The finding that network-refereed workers were more productive than nonreferred workers is generally consistent with the existing literature, especially of
those studying the role of informal job networks in high-paying sectors, (e.g., Si20

The estimation results of the pooled regression are quite comparable to those of the fixed-effectregression, so the conclusion that we draw from the fixed-effect regression results still holds. The
estimation results of the pooled regression are available upon request from the authors.

24mon and Warner, 1992; Kugler, 2003; Antoninis, 2006; Loury, 2006). This paper
further presented new empirical evidence that workers and firms may rely on two
mechanisms of the informal networks at the same time in their job search and hiring processes; that is, workers use the network to reduce the uncertainty in theirmatch value with potential employers, and firms use the network to select highability workers who are referred by high-ability referees. Of course, the evidence
described above is derived from the labor market of patent inventors who have very
high skills in general, and thus the implications may not be as valid for the labormarkets of low-skilled workers. Although we expect the difference will be only
a matter of degree, additional empirical evidence is necessary to ensure that the
network mechanisms described here still work in the more general labor market
setting.
The results of this paper have several implications for future research. First,
although it is consistently suggested that the network- recruitment method enables
firms to hire better-matched inventors than the publicly recruitment method does,
it is found that many inventors were not necessary recruited through network reference. This raises the possibility that firms self-select themselves into segregatedlabor markets in which some firms use the network-recruitment method and otherfirms use the public recruitment method. This paper assumes that firms’ choice ofrecruitment method is exogenously given by some process unrelated to search andmatching, and thus does not explicitly account for firms’ endogenous use of employees’ referral in hiring inventors. Therefore, future work is needed to address
this issue.
Second, our findings indicated that firms may use interfirm R&D networks not
only as a way of exploiting external knowledge, as previously reported by the industrial organization literature (e.g., Singh, 2005), but also as a way of recruiting
inventors with good match values and high research abilities through inventors’referrals. This unique role of R&D networks, which was downplayed by previous
studies, may explain the observed relationship between firms’ inventive productivity and their network position in R&D networks (Ahuja, 2000; Schilling andPhelps, 2007). The empirical finding of this paper suggests that firms with higher
connectivity in R&D networks can recruit better-matched inventors than those withlow connectivity, because the former firms can use more interfirm referrals than thelatter firms in their hiring processes. Thus, a future extension of this paper will examine this hypothesis, and it will be a step forward in understanding the mechanismof how the global R&D network structure influences firm-level innovation.

25

CJS1 (Spell 1)

Assignee 1

CJS2 (Spell 2)

Assignee 2

CJS3

Assignee 3

CJS4

Assignee 4

CJS5

Assignee 5
P11

P12

P13

P21

P31

P32

P22

P41 P51

Figure 1: The Construction of Job Spells

26

P42 P52

ApplicationDatepublicly recruited

network recruited

1

0.9

0.8

0.7

Probability

0.6

0.5

0.4

0.3

0.2

0.1

0
0

50

100

150

200

250

Employment Duration

Figure 2: Survival Curves for Employment Duration after Job Transition27

Table 1: Median Employment Durations
OVERALLC OLLBORATION
Publicly Recruited
Network Recruited
Log-Rank

71
93-10.33(3.56)L IMITED I NTERVAL†C OLLABORATIONW ITHIN 36 W ITHIN 24
W ITHIN 12
75
78
82
110
117
124
-8.16
-7.99
-5.32
(2.84)(2.56)(1.73)N OTE .– Standard errors are in parentheses. All test results are statistically significant
for χ2 test at less than one percent level (p < .01). † Collaborations from limited
intervals before the job switch are considered. The Intervals are within 36 months, 24months, and 12 months before the job switch.

28

Table 2: Weibull Hazard Regression Results
OVERALLC OLLBORATION

Network referral dummy: δField dummies:
ChemicalComp.& Comm.
Drugs & Medical
Elec. & Electronics
Mechanical
OthersPast productivity:
Total

(1)
−.5058(.031)L IMITED I NTERVALC OLLABORATION†W ITHIN 36 W ITHIN 24 W ITHIN 12
(2)
(3)
(4)
(5)
−.4675−.4325−.4269−.4395(.030)(.039)(.044)(.060)−1.2045(.031)−1.0751(.035)−1.0293(.039)−1.1066(.031)−1.1505(.030)−1.0775(.031)−1.1882(.031)−1.0771(.035)−1.0223(.039)−1.1008(.031)−1.1476(.030)−1.0782(.030).0287(.002)

AverageResearch experience(Research experience)2
log α aConstant
Log-likelihood
Observations−.0893(.009).0036(.000).4142(.010)−5.0659(.074)-1648933178.0326(.004)−.0819(.009).0035(.000).4125(.010)−5.0755(.074)-1651033178−1.2110(.031)−1.0783(.035)−1.0362(.038)−1.1000(.031)−1.1476(.030)−1.0763(.030)−1.2136(.031)−1.0782(.035)−1.0367(.038)−1.1003(.031)−1.1490(.030)−1.0769(.030)−1.2171(.031)−1.0783(.035)−1.0387(.038)−1.0996(.031)−1.1489(.030)−1.0748(.030).0273(.002).0269(.002).0259(.002)−.0965(.009).0039(.000).4183(.010)−5.1001(.075)-1657333178−.0958(.009).0039(.000).4200(.010)−5.1243(.074)-1659233178−.0900(.009).0037(.000).4231(.010)−5.1809(.074)-1661933178

N OTE .– Robust standard errors are in parentheses. All variables are statistically significant at lessthan one percent level (p < .01).
a
The duration dependence is represented by parameter α.
† Collaborations from limited intervals before the job switch are considered. The Intervals arewithin 36 months, 24 months, and 12 months before the job switch.

29

Table 3: Summary Statistics
A LL S AMPLESVARIABLE
Number of patents made by
an individual in a year
Network referral dummies:
overallwithin 36 months
within 24 months
within 12 months
Referee’s productivity
Past productivity
Total research experience
Within-firm research experienceFirm’s productivity†
Research field dummies:
ChemicalComp.& Comm.
Drugs & Medical
Elec. & Electronics
Mechanical
OthersN

N ETWORKR ECRUITED
1.126(1.21)P UBLICLYR ECRUITED
0.989(1.07)

0.102(.303)
0.064(.245)
0.050(.217)
0.026(.159)

–

–

–

–

–

–

–

–

1.826
(10.615)
1.716(3.706)5.713(5.353)3.344(3.176)0.088(.185)

17.831(28.544)
4.769(6.338)8.109(5.269)3.045(2.653)0.078(.183)

0.000(.000)
1.368(3.087)5.439(5.294)3.378(3.229)0.090(.186)

0.316(.465)
0.194(.395)
0.131(.337)
0.288(.453)
0.307(.461)
0.295(.456)
286954.000

0.349(.477)
0.182(.386)
0.178(.383)
0.248(.432)
0.273(.446)
0.265(.441)
29393.000

0.312(.463)
0.195(.396)
0.125(.331)
0.293(.455)
0.311(.463)
0.298(.458)
257561.000

1.003(1.08)N OTE .– The statistics are computed for both the aggregated samples and disaggregated samples by recruitment method. The standard errors are in parentheses.
† The unit of firm’s productivity is in thousand.

30

Table 4: Baseline Regression Estimates

Network referral dummy: ρ

Productivity
Productivity Growth
Regressions
Regression
OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12 OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.0554∗∗∗ 0.0685∗∗∗ 0.0733∗∗∗ 0.0733∗∗∗(0.0072) (0.0082) (0.0092) (0.0122)

Interaction terms of networkand tenure dummies: ρkyear 10.0355∗∗∗ 0.0368∗∗∗ 0.0425∗∗∗ 0.0437∗∗∗(0.0074) (0.0089) (0.0102) (0.0145)
0.0493∗∗∗ 0.0994∗∗∗ 0.1040∗∗∗ 0.1245∗∗∗(0.0182) (0.0219) (0.0248) (0.0351)
0.1449∗∗∗ 0.1883∗∗∗ 0.1909∗∗∗ 0.1911∗∗∗(0.0198) (0.0245) (0.0266) (0.0332)
0.1103∗∗∗ 0.1288∗∗∗ 0.1258∗∗∗ 0.1191∗∗∗(0.0247) (0.0297) (0.0333) (0.0435)
0.0743∗∗ 0.0728∗
0.0707 −0.0195(0.0323) (0.0422) (0.0499) (0.0477)
0.0731∗∗ 0.0675
0.04710.0574(0.0371) (0.0468) (0.0559) (0.0775)
0.07000.0264 −0.0147 −0.0760(0.0503) (0.0660) (0.0810) (0.0840)
0.07230.08170.08880.0988(0.0522) (0.0681) (0.0691) (0.0906)−0.0732 −0.0688
0.01650.0070(0.0650) (0.0788) (0.0884) (0.1264)−0.0768 −0.0332 −0.0208 −0.1360

year 2
year 3
year 4
year 5year 6
year 7
year 8year 9year 100.0014∗∗∗ 0.0015∗∗∗ 0.0016∗∗∗ 0.0018∗∗∗ 0.0014∗∗∗ 0.0015∗∗∗ 0.0016∗∗∗ 0.0018∗∗∗(0.0002) (0.0002) (0.0002) (0.0002)(0.0002) (0.0002) (0.0002) (0.0002)
Past patent productivity
0.0103∗∗∗ 0.0102∗∗∗ 0.0102∗∗∗ 0.0103∗∗∗ 0.0102∗∗∗ 0.0102∗∗∗ 0.0102∗∗∗ 0.0103∗∗∗(0.0006) (0.0006) (0.0006) (0.0006)(0.0006) (0.0006) (0.0006) (0.0006)
Overall research experience−0.0108∗∗∗−0.0101∗∗∗−0.0098∗∗∗−0.0097∗∗∗ −0.0109∗∗∗−0.0103∗∗∗−0.0099∗∗∗−0.0097∗∗∗(0.0011) (0.0011) (0.0011) (0.0011)(0.0011) (0.0011) (0.0011) (0.0011)(Overall research experience)2
0.0003∗∗∗ 0.0003∗∗∗ 0.0002∗∗∗ 0.0002∗∗∗ 0.0003∗∗∗ 0.0003∗∗∗ 0.0002∗∗∗ 0.0002∗∗∗(0.0001) (0.0001) (0.0001) (0.0001)(0.0001) (0.0001) (0.0001) (0.0001)
Within-firm research experience −0.0039 −0.0044 −0.0046 −0.0048−0.0034 −0.0028 −0.0034 −0.0038(0.0033) (0.0033) (0.0033) (0.0033)(0.0035) (0.0035) (0.0034) (0.0034)(Within-firm research experience)2 0.0003∗
0.0003∗0.0004∗0.0004∗0.0004∗0.00030.0004∗0.0003∗(0.0002) (0.0002) (0.0002) (0.0002)(0.0002) (0.0002) (0.0002) (0.0002)
The first tenure year dummy
0.4603∗∗∗ 0.4601∗∗∗ 0.4600∗∗∗ 0.4592∗∗∗ 0.4687∗∗∗ 0.4709∗∗∗ 0.4681∗∗∗ 0.4643∗∗∗(0.0075) (0.0075) (0.0075) (0.0075)(0.0080) (0.0078) (0.0077) (0.0076)
Firm’s productivity
0.1685∗∗∗ 0.1692∗∗∗ 0.1691∗∗∗ 0.1681∗∗∗ 0.1680∗∗∗ 0.1679∗∗∗ 0.1679∗∗∗ 0.1670∗∗∗(0.0109) (0.0109) (0.0109) (0.0109)(0.0109) (0.0109) (0.0109) (0.0109)
Constant−0.3314∗∗∗−0.3320∗∗∗−0.3313∗∗∗−0.3286∗∗∗ −0.3402∗∗∗−0.3444∗∗∗−0.3408∗∗∗−0.3350∗∗∗(0.0124) (0.0124) (0.0124) (0.0124)(0.0130) (0.0128) (0.0127) (0.0126)
Log-likelihood-355398.98 -355393.13 -355395.49 -355410.54 -355355.05 -355346.29 -355358.24 -355384.75
Observations
286954286954286954286954286954286954286954286954

Referee’s productivity

N OTE .– All estimation results are presented in Appendix C. Other variable included in each column are the same as those in Table
C1. Collaborations from limited intervals before the job switch are considered. The Intervals are within 36 months, 24 months, and12 months before the job switch. Robust standard errors are in parentheses. ∗ p < .1. ∗ ∗ p < .05. ∗ ∗ ∗ p < .01.

31

Table 5: Fixed-effect Regression Estimates

Network referral dummy: ρ

Productivity
Productivity Growth
Regressions
Regression
OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12 OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.1023∗∗∗ 0.1270∗∗∗ 0.1551∗∗∗ 0.1806∗∗∗(0.0104) (0.0117) (0.0129) (0.0169)

Interaction terms of networkand tenure dummies: ρkyear 10.0801∗∗∗ 0.0935∗∗∗ 0.1195∗∗∗ 0.1368∗∗∗(0.0120) (0.0138) (0.0154) (0.0208)
year 20.1323∗∗∗ 0.1844∗∗∗ 0.2133∗∗∗ 0.2599∗∗∗(0.0172) (0.0199) (0.0220) (0.0287)
year 30.2166∗∗∗ 0.2640∗∗∗ 0.2865∗∗∗ 0.3101∗∗∗(0.0186) (0.0221) (0.0244) (0.0322)
year 40.1589∗∗∗ 0.1794∗∗∗ 0.1980∗∗∗ 0.2250∗∗∗(0.0226) (0.0279) (0.0312) (0.0417)
year 50.1026∗∗∗ 0.1120∗∗∗ 0.1329∗∗∗ 0.0793(0.0271) (0.0338) (0.0384) (0.0525)
year 60.0907∗∗∗ 0.1111∗∗∗ 0.1238∗∗∗ 0.1755∗∗∗(0.0320) (0.0406) (0.0468) (0.0613)
year 7
0.06190.03850.04260.0224(0.0377) (0.0488) (0.0569) (0.0784)
year 80.05710.08650.1331∗∗ 0.1784∗∗(0.0436) (0.0541) (0.0617) (0.0816)
year 9−0.1158∗∗ −0.0836
0.01670.0399(0.0549) (0.0678) (0.0751) (0.1014)
year 10−0.1161∗ −0.0655 −0.0398 −0.1252(0.0631) (0.0759) (0.0865) (0.1262)
Referee’s productivity
0.0012∗∗∗ 0.0014∗∗∗ 0.0014∗∗∗ 0.0017∗∗∗ 0.0011∗∗∗ 0.0014∗∗∗ 0.0014∗∗∗ 0.0017∗∗∗(0.0002) (0.0002) (0.0002) (0.0002)(0.0002) (0.0002) (0.0002) (0.0002)
Past productivity−0.0394∗∗∗−0.0395∗∗∗−0.0395∗∗∗−0.0392∗∗∗ −0.0395∗∗∗−0.0396∗∗∗−0.0396∗∗∗−0.0392∗∗∗(0.0008) (0.0008) (0.0008) (0.0008)(0.0008) (0.0008) (0.0008) (0.0008)
Total research experience
0.0159∗∗∗ 0.0167∗∗∗ 0.0170∗∗∗ 0.0177∗∗∗ 0.0153∗∗∗ 0.0162∗∗∗ 0.0167∗∗∗ 0.0175∗∗∗(0.0015) (0.0015) (0.0015) (0.0015)(0.0015) (0.0015) (0.0015) (0.0015)(Total research experience)20.0011∗∗∗ 0.0010∗∗∗ 0.0010∗∗∗ 0.0010∗∗∗ 0.0011∗∗∗ 0.0011∗∗∗ 0.0011∗∗∗ 0.0010∗∗∗(0.0001) (0.0001) (0.0001) (0.0001)(0.0001) (0.0001) (0.0001) (0.0001)
Within-firm research experience
0.0062∗∗ 0.0053∗
0.0052∗0.00480.0101∗∗∗ 0.0086∗∗∗ 0.0076∗∗ 0.0066∗∗(0.0031) (0.0031) (0.0031) (0.0031)(0.0033) (0.0032) (0.0032) (0.0031)(Within-firm research experience)2−0.0007∗∗∗−0.0007∗∗∗−0.0007∗∗∗−0.0006∗∗∗ −0.0008∗∗∗−0.0007∗∗∗−0.0007∗∗∗−0.0007∗∗∗(0.0002) (0.0002) (0.0002) (0.0002)(0.0002) (0.0002) (0.0002) (0.0002)
The first tenure year dummy
0.5396∗∗∗ 0.5394∗∗∗ 0.5396∗∗∗ 0.5390∗∗∗ 0.5569∗∗∗ 0.5548∗∗∗ 0.5515∗∗∗ 0.5465∗∗∗(0.0071) (0.0071) (0.0071) (0.0071)(0.0076) (0.0074) (0.0073) (0.0072)
Firm’s productivity
0.3045∗∗∗ 0.3061∗∗∗ 0.3072∗∗∗ 0.3074∗∗∗ 0.3024∗∗∗ 0.3036∗∗∗ 0.3055∗∗∗ 0.3054∗∗∗(0.0172) (0.0173) (0.0173) (0.0172)(0.0172) (0.0172) (0.0172) (0.0172)
Log-likelihood-236433.22 -236423.18 -236410.06 -236425.31 -236363.28 -236356.18 -236358.39 -236393.13
Observations
286954286954286954286954286954286954286954286954N OTE .– Dependent variable is the number of patents applied by inventor. All estimation results are presented in Appendix C. Other
variable included in each column are the same as those in Table C1. Collaborations from limited intervals before the job switch areconsidered. The Intervals are within 36 months, 24 months, and 12 months before the job switch. ∗ p < .1. ∗∗ p < .05. ∗∗∗ p < .01.

32

Table 6: Fixed-effect Regression Using Coauthorship-Adjusted Productivity

Network referral dummy: ρ

Productivity
Productivity Growth
Regressions
Regression
OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12 OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.0586∗∗∗ 0.0564∗∗∗ 0.0732∗∗∗ 0.0937∗∗∗(0.0071) (0.0081) (0.0089) (0.0117)

Interaction terms of networkand tenure dummies: ρkyear 1−0.0115 −0.0189∗ −0.0072
0.0009(0.0086) (0.0100) (0.0113) (0.0154)
year 20.1125∗∗∗ 0.1213∗∗∗ 0.1384∗∗∗ 0.1674∗∗∗(0.0108) (0.0128) (0.0142) (0.0188)
year 30.1558∗∗∗ 0.1651∗∗∗ 0.1789∗∗∗ 0.1971∗∗∗(0.0121) (0.0147) (0.0163) (0.0217)
year 40.1466∗∗∗ 0.1404∗∗∗ 0.1587∗∗∗ 0.1875∗∗∗(0.0145) (0.0181) (0.0203) (0.0273)
year 50.1165∗∗∗ 0.0974∗∗∗ 0.1148∗∗∗ 0.1106∗∗∗(0.0171) (0.0215) (0.0244) (0.0325)
year 60.1071∗∗∗ 0.0922∗∗∗ 0.0985∗∗∗ 0.1104∗∗∗(0.0202) (0.0256) (0.0293) (0.0390)
year 7
0.0926∗∗∗ 0.0723∗∗ 0.0776∗∗ 0.0866∗(0.0239) (0.0305) (0.0349) (0.0471)
year 80.1299∗∗∗ 0.1321∗∗∗ 0.1716∗∗∗ 0.1862∗∗∗(0.0278) (0.0348) (0.0399) (0.0535)
year 90.02870.01400.07570.1106∗(0.0330) (0.0408) (0.0469) (0.0636)
year 100.03520.04710.0766 −0.0155(0.0381) (0.0464) (0.0531) (0.0736)
Referee’s productivity
0.0010∗∗∗ 0.0013∗∗∗ 0.0013∗∗∗ 0.0014∗∗∗ 0.0010∗∗∗ 0.0013∗∗∗ 0.0013∗∗∗ 0.0014∗∗∗(0.0002) (0.0002) (0.0002) (0.0002)(0.0002) (0.0002) (0.0002) (0.0002)
Past productivity−0.0355∗∗∗−0.0354∗∗∗−0.0355∗∗∗−0.0354∗∗∗ −0.0358∗∗∗−0.0356∗∗∗−0.0356∗∗∗−0.0354∗∗∗(0.0006) (0.0006) (0.0006) (0.0006)(0.0006) (0.0006) (0.0006) (0.0006)
Total research experience
0.0045∗∗∗ 0.0051∗∗∗ 0.0052∗∗∗ 0.0055∗∗∗ 0.0053∗∗∗ 0.0053∗∗∗ 0.0053∗∗∗ 0.0055∗∗∗(0.0010) (0.0010) (0.0010) (0.0010)(0.0010) (0.0010) (0.0010) (0.0010)(Total research experience)2
0.0005∗∗∗ 0.0005∗∗∗ 0.0005∗∗∗ 0.0005∗∗∗ 0.0005∗∗∗ 0.0005∗∗∗ 0.0005∗∗∗ 0.0005∗∗∗(0.0001) (0.0001) (0.0001) (0.0001)(0.0001) (0.0001) (0.0001) (0.0001)
Within-firm research experience
0.00090.00030.00030.00010.00140.00130.00100.0007(0.0019) (0.0019) (0.0019) (0.0019)(0.0020) (0.0019) (0.0019) (0.0019)(Within-firm research experience)2−0.0005∗∗∗−0.0005∗∗∗−0.0005∗∗∗−0.0005∗∗∗ −0.0005∗∗∗−0.0005∗∗∗−0.0005∗∗∗−0.0005∗∗∗(0.0001) (0.0001) (0.0001) (0.0001)(0.0001) (0.0001) (0.0001) (0.0001)
The first tenure year dummy
0.3475∗∗∗ 0.3471∗∗∗ 0.3472∗∗∗ 0.3470∗∗∗ 0.3672∗∗∗ 0.3612∗∗∗ 0.3583∗∗∗ 0.3535∗∗∗(0.0044) (0.0044) (0.0044) (0.0044)(0.0047) (0.0046) (0.0045) (0.0045)
Firm’s productivity
0.1334∗∗∗ 0.1346∗∗∗ 0.1349∗∗∗ 0.1348∗∗∗ 0.1324∗∗∗ 0.1330∗∗∗ 0.1337∗∗∗ 0.1336∗∗∗(0.0117) (0.0117) (0.0117) (0.0117)(0.0117) (0.0117) (0.0117) (0.0117)
Log-likelihood-249117.45 -249129.52 -249118.27 -249120.36 -248953.42 -249003.83 -249015.49 -249052.41
Observations
286954286954286954286954286954286954286954286954N OTE .– Dependent variable is the annual sum of inventor’s per coauthor contribution to patent. Fixed effect regressions are used.
All estimation results are presented in Appendix C. Other variable included in each column are the same as those in Table C1.
Collaborations from limited intervals before the job switch are considered. The Intervals are within 36 months, 24 months, and 12
months before the job switch. Robust standard errors are in parentheses. ∗ p < .1. ∗ ∗ p < .05. ∗ ∗ ∗ p < .01.

33

A Appendix: Computation of the Mean Productivity
The denominator of the conditional density (11) is computed by:% %∗∗Prob [(q > q ) ∩ (θ > θ )] =φ ((θ − q)/s) φ ((q − µ)/σqk ) dqdθ.
θ∗q∗Therefore, the mean productivity in the second period is given by:
(
(
θ
∗
θ
q ∗ φ ((θ − q)/s) φ ((q − µ)/σqk ) dqdθE(θ|q > q ∗ , θ > θ ∗ ) =Prob [(q > q ∗ ) ∩ (θ > θ ∗ )]((θ∗ θ q ∗ φ ((θ − q)/s) φ ((q − µ)/σqk ) dqdθ= ( (θ∗ q ∗ φ ((θ − q)/s) φ ((q − µ)/σqk ) dqdθ
4
( 3(
q∗
θ∗ θφ ((θ − q)/s) dθ φ ((q − µ)/σqk ) dq
4
= ( 3(.(16)
q∗
θ∗ φ ((θ − q)/s) dθ φ ((q − µ)/σqk ) dq

We can use the following equalities:%φ ((θ − q)/s) dθ = Φ ((µ − q)/σqk ) ,∗% θ
θφ ((θ − q)/s) dθ = q + sλ ((q − µ)/σqk ) .
θ∗We obtain equation (12) by substituting these equations into equation (16)B Computerized Matching Process
The essence of the computerized matching process (CMP) proposed by Trajtenberg, Shiff, and Melamed (2006) is to adjust for possible spelling errors in inventors’ names listed in patents in order to avoid identifying an inventor as two
different inventors while minimizing the possibilities of identifying two different
inventors as the same person by utilizing other information such as addresses, assignees, and patent classes.
The former is done by converting the last name and first name of the listed inventors into “soundex” codes following the rule described in Trajtenberg, Shiff, andMelamed (2006, p. 17, Table 3.1). This conversion allows us to group inventors
whose names are spelled in a similar manner into one depending on the numbers
assigned to them. We then utilize other information to distinguish inventors withthe same “soundex” code. The information we employ in matching inventors are(1) full address, (2) self-citations, (3) shared collaborators, (4) middle names, (5)34surname modifier, (6) assignee, (7) city, and (8) patent class. Each information
gives a score to a pair of names (soundex code), and depending on the total scoreobtained, we decide whether two inventors are the same person or not. The namematching criteria we have employed are summarized in Table B. 1. While criterion
A follows Trajtenberg, Shiff, and Melamed (2006), criterion B is more stringent.
Let us describe the scoring procedure in more detail below.
When full street addresses are identical between the inventors listed in two
different patents, the pair obtains a score of 120.21 When a patent is citing an older
patent applied for by the inventor with a similar soundex code, then we consider
that these two patents are applied for by the same person (self-citation) and the pairof names obtains a score of 120.22 In addition, if two patents are each applied for by
two or more inventors and one of them is identified individually, then the remaining
inventors are considered to have collaborated with the identified inventor. If there
is a pair of inventors (listed in both patents) who have similar soundex codes, thenwe consider them to be one person who has collaborated repeatedly with an alreadyidentified inventor (shared collaborators) and this inventor obtains a score of 120.
If the pair of records share more than two letters from the middle name (fullmiddle name), a score of 100 is given, and if they share the same surname modifier,they get a score of 50. In the case where two records share only the middle nameinitials, assignee, city, or patent classes, the score depends on whether such records
are “rare” or not. Namely, we assume, for example, that a city is “rare,” if thenumber of records that share the same city is smaller than the cutoff value. The
cutoff value is set to the median of the frequency distribution of the city name.
Otherwise, it is considered to be “common.” If a pair of records shares either the
middle name initials, assignee, or city, it obtains a score of 100 if it is considered
to be “rare” and 80 if it is “common”. In the case of patent class, the pair obtains ascore of 80 or 50, respectively. These scores are summarized in Table B. 2
We also consider the cases in which we categorize the names to be “rare” or“common” as in Trajtenberg, Shiff, and Melamed (2006). Similarly to the casesof cities and assignees, a name is considered to be “rare,” if it appears less frequently in the data than the median of the frequency distribution. Furthermore,when a name is considered to be “rare”, the likelihood that two records correspondto one inventor is higher. Therefore, less strict criteria are set for other information.
Namely, the middle name initials, assignee, city, and patent class obtain a higherscore (the one corresponding to “rare” cases) if they are below the 75 percentile ofthe frequency distribution, instead of the median.
In total, we consider four cases, depending on which matching criterion is uti21
22

It should be noted that the value of the score itself has no significant meaning.
There are only 121 pairs of patents that satisfy this criterion.

35

lized and whether rareness of names is considered or not. The results of the fourcases are summarized in Table B. 3. As the table shows, the four cases we haveconsidered do not differ substantially in terms of number of unique inventors identified. The procedure that uses criteria A and treats “rare” names differently (second
row in the table) identifies the least number of inventors; however the difference
between the one that identifies the highest number of inventors (the one using criteria B and does not treat “rare” names differently, reported in the third row in thetable) is less than one percent of the total number of inventors identified. Our analysis is based on the procedure that uses criteria A and does not treat “rare” names
differently (the base line case reported in the first row).

36

Table B. 1: Matching Criteria in CMP
Criteria A (Trajtenberg, Shiff, and Melamed, 2006)(1) Identical Last name and First name, and non zero partof soundex code is more than 5 digits(2) Identical Last name, and non zero part of soundex code
is more than 2 digits(3)Others
Criteria B (this paper)(1) Identical Last, First, and middle name, and non zero partof soundex code is more than 5 digits(2) Identical Last name, and non zero part of soundex code
is more than 2 digits(3) Others

Cutoff value
100
120
180
Cutoff value
100
120
180

Table B. 2: List of Scores in CMP
score
120
120
120
100
50
rare common
100
80
100
80
100
80
80
50

Full Address
Self Citation
Shared Partners
Full middle name
Surname Modifier
Middle name initial
AssigneeCity
Patent Class

Table B. 3: CMP Results

Criteria A
Criteria A
Criteria B
Criteria B

cutoff values
for rare names for common names
50
50
75
50
50
50
75
50

37

Number of
unique inventors
746,991
744,381748,646746,368

Table C. 1: Baseline Regression: All Estimation Results

Network referral dummy: ρ

Productivity
Productivity Growth
Regressions
Regression
OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12 OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.0554∗∗∗ 0.0685∗∗∗ 0.0733∗∗∗ 0.0733∗∗∗(0.0072) (0.0082) (0.0092) (0.0122)

Interaction terms of networkand tenure dummies: ρkyear 10.0355∗∗∗ 0.0368∗∗∗ 0.0425∗∗∗ 0.0437∗∗∗(0.0074) (0.0089) (0.0102) (0.0145)
0.0493∗∗∗ 0.0994∗∗∗ 0.1040∗∗∗ 0.1245∗∗∗(0.0182) (0.0219) (0.0248) (0.0351)
0.1449∗∗∗ 0.1883∗∗∗ 0.1909∗∗∗ 0.1911∗∗∗(0.0198) (0.0245) (0.0266) (0.0332)
0.1103∗∗∗ 0.1288∗∗∗ 0.1258∗∗∗ 0.1191∗∗∗(0.0247) (0.0297) (0.0333) (0.0435)
0.0743∗∗ 0.0728∗
0.0707 −0.0195(0.0323) (0.0422) (0.0499) (0.0477)
0.0731∗∗ 0.0675
0.04710.0574(0.0371) (0.0468) (0.0559) (0.0775)
0.07000.0264 −0.0147 −0.0760(0.0503) (0.0660) (0.0810) (0.0840)
0.07230.08170.08880.0988(0.0522) (0.0681) (0.0691) (0.0906)−0.0732 −0.0688
0.01650.0070(0.0650) (0.0788) (0.0884) (0.1264)−0.0768 −0.0332 −0.0208 −0.1360(0.0723) (0.0916) (0.1106) (0.1507)−0.0263 −0.0626
0.00280.0957(0.0960) (0.1291) (0.1172) (0.1594)−0.1136 −0.1473 −0.1543 −0.1058(0.1008) (0.1282) (0.1506) (0.1868)−0.1558 −0.1953 −0.1009 −0.1950(0.1085) (0.1336) (0.1560) (0.2086)−0.1398 −0.1900 −0.2250 −0.2290(0.1482) (0.1492) (0.1862) (0.2663)−0.1647 −0.2366 −0.1947 −0.0582(0.1762) (0.1876) (0.2131) (0.2711)
0.3348 −0.1901 −0.3310 −0.1520(0.3904) (0.2933) (0.3322) (0.4880)−0.4982∗∗ −0.4558∗ −0.6362∗∗ −0.5160(0.2080) (0.2406) (0.2670) (0.3618)−0.1268 −0.1412
0.0136 −0.4545(0.2017) (0.2261) (0.2186) (0.4116)−0.6782∗ −0.6764∗ −0.9025 −0.6961(0.3818) (0.3819) (0.6033) (0.5643)−0.1027 −0.0990 −0.0953 −0.0515(0.0993) (0.0994) (0.0995) (0.1085)−1.0322∗ −1.0263∗ −1.0217∗ −1.0143∗

year 2
year 3
year 4
year 5year 6
year 7
year 8year 9year 10year 11year 12year 13
year 14
year 15
year 16year 17year 18
year 19
year 20year 2138

(Continued from previous page)

Productivity
Regressions
OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12
(1)
(2)
(3)
(4)

Productivity Growth
Regression
OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12
(5)
(6)
(7)
(8)
(0.5547) (0.5547) (0.5547) (0.5546)
year 22−0.3985∗∗ −0.3920∗∗ −0.3870∗∗ −0.3775∗∗(0.1598) (0.1599) (0.1598) (0.1599)
Referee’s productivity
0.0014∗∗∗ 0.0015∗∗∗ 0.0016∗∗∗ 0.0018∗∗∗ 0.0014∗∗∗ 0.0015∗∗∗ 0.0016∗∗∗ 0.0018∗∗∗(0.0002) (0.0002) (0.0002) (0.0002)(0.0002) (0.0002) (0.0002) (0.0002)
Past productivity
0.0103∗∗∗ 0.0102∗∗∗ 0.0102∗∗∗ 0.0103∗∗∗ 0.0102∗∗∗ 0.0102∗∗∗ 0.0102∗∗∗ 0.0103∗∗∗(0.0006) (0.0006) (0.0006) (0.0006)(0.0006) (0.0006) (0.0006) (0.0006)
Total research experience−0.0108∗∗∗−0.0101∗∗∗−0.0098∗∗∗−0.0097∗∗∗ −0.0109∗∗∗−0.0103∗∗∗−0.0099∗∗∗−0.0097∗∗∗(0.0011) (0.0011) (0.0011) (0.0011)(0.0011) (0.0011) (0.0011) (0.0011)(Total research experience)2
0.0003∗∗∗ 0.0003∗∗∗ 0.0002∗∗∗ 0.0002∗∗∗ 0.0003∗∗∗ 0.0003∗∗∗ 0.0002∗∗∗ 0.0002∗∗∗(0.0001) (0.0001) (0.0001) (0.0001)(0.0001) (0.0001) (0.0001) (0.0001)
Within-firm research experience −0.0039 −0.0044 −0.0046 −0.0048−0.0034 −0.0028 −0.0034 −0.0038(0.0033) (0.0033) (0.0033) (0.0033)(0.0035) (0.0035) (0.0034) (0.0034)(Within-firm research experience)2 0.0003∗
0.0003∗0.0004∗0.0004∗0.0004∗0.00030.0004∗0.0003∗(0.0002) (0.0002) (0.0002) (0.0002)(0.0002) (0.0002) (0.0002) (0.0002)
The first tenure year dummy
0.4603∗∗∗ 0.4601∗∗∗ 0.4600∗∗∗ 0.4592∗∗∗ 0.4687∗∗∗ 0.4709∗∗∗ 0.4681∗∗∗ 0.4643∗∗∗(0.0075) (0.0075) (0.0075) (0.0075)(0.0080) (0.0078) (0.0077) (0.0076)
Firm’s productivity
0.1685∗∗∗ 0.1692∗∗∗ 0.1691∗∗∗ 0.1681∗∗∗ 0.1680∗∗∗ 0.1679∗∗∗ 0.1679∗∗∗ 0.1670∗∗∗(0.0109) (0.0109) (0.0109) (0.0109)(0.0109) (0.0109) (0.0109) (0.0109)
Field dummies:
Chemical
0.2910∗∗∗ 0.2910∗∗∗ 0.2911∗∗∗ 0.2912∗∗∗ 0.2915∗∗∗ 0.2916∗∗∗ 0.2916∗∗∗ 0.2916∗∗∗(0.0049) (0.0049) (0.0049) (0.0049)(0.0049) (0.0049) (0.0049) (0.0049)
Comp.& Comm.
0.2304∗∗∗ 0.2304∗∗∗ 0.2304∗∗∗ 0.2303∗∗∗ 0.2306∗∗∗ 0.2306∗∗∗ 0.2306∗∗∗ 0.2304∗∗∗(0.0057) (0.0057) (0.0057) (0.0057)(0.0057) (0.0057) (0.0057) (0.0057)
Drugs & Medical
0.2885∗∗∗ 0.2882∗∗∗ 0.2880∗∗∗ 0.2882∗∗∗ 0.2888∗∗∗ 0.2884∗∗∗ 0.2880∗∗∗ 0.2882∗∗∗(0.0075) (0.0075) (0.0075) (0.0075)(0.0075) (0.0075) (0.0075) (0.0075)
Elec. & Electronics
0.1908∗∗∗ 0.1908∗∗∗ 0.1907∗∗∗ 0.1908∗∗∗ 0.1910∗∗∗ 0.1909∗∗∗ 0.1909∗∗∗ 0.1909∗∗∗(0.0048) (0.0048) (0.0048) (0.0048)(0.0048) (0.0048) (0.0048) (0.0048)
Mechanical0.1888∗∗∗ 0.1889∗∗∗ 0.1889∗∗∗ 0.1890∗∗∗ 0.1891∗∗∗ 0.1892∗∗∗ 0.1892∗∗∗ 0.1891∗∗∗(0.0047) (0.0047) (0.0047) (0.0047)(0.0047) (0.0047) (0.0047) (0.0047)
Others
0.1790∗∗∗ 0.1790∗∗∗ 0.1791∗∗∗ 0.1789∗∗∗ 0.1793∗∗∗ 0.1792∗∗∗ 0.1792∗∗∗ 0.1788∗∗∗(0.0051) (0.0051) (0.0051) (0.0051)(0.0051) (0.0051) (0.0051) (0.0051)
Annual dummies
year 2−0.1430∗∗∗−0.1421∗∗∗−0.1427∗∗∗−0.1444∗∗∗ −0.1406∗∗∗−0.1388∗∗∗−0.1400∗∗∗−0.1427∗∗∗(0.0133) (0.0133) (0.0133) (0.0133)(0.0133) (0.0133) (0.0133) (0.0133)
year 3−0.1929∗∗∗−0.1923∗∗∗−0.1929∗∗∗−0.1943∗∗∗ −0.1897∗∗∗−0.1883∗∗∗−0.1898∗∗∗−0.1926∗∗∗(0.0145) (0.0145) (0.0145) (0.0145)(0.0145) (0.0145) (0.0145) (0.0145)
year 4−0.2377∗∗∗−0.2372∗∗∗−0.2378∗∗∗−0.2391∗∗∗ −0.2344∗∗∗−0.2335∗∗∗−0.2349∗∗∗−0.2375∗∗∗(0.0152) (0.0152) (0.0152) (0.0152)(0.0152) (0.0152) (0.0152) (0.0152)
year 5−0.2435∗∗∗−0.2431∗∗∗−0.2436∗∗∗−0.2450∗∗∗ −0.2405∗∗∗−0.2399∗∗∗−0.2411∗∗∗−0.2434∗∗∗(0.0146) (0.0146) (0.0146) (0.0146)(0.0146) (0.0146) (0.0146) (0.0146)
year 6−0.2665∗∗∗−0.2661∗∗∗−0.2666∗∗∗−0.2679∗∗∗ −0.2637∗∗∗−0.2632∗∗∗−0.2643∗∗∗−0.2665∗∗∗(0.0135) (0.0135) (0.0135) (0.0135)(0.0135) (0.0135) (0.0135) (0.0135)
year 7−0.2905∗∗∗−0.2903∗∗∗−0.2909∗∗∗−0.2921∗∗∗ −0.2878∗∗∗−0.2874∗∗∗−0.2885∗∗∗−0.2906∗∗∗(0.0130) (0.0130) (0.0130) (0.0130)(0.0130) (0.0130) (0.0130) (0.0130)
year 8−0.3261∗∗∗−0.3257∗∗∗−0.3262∗∗∗−0.3275∗∗∗ −0.3236∗∗∗−0.3232∗∗∗−0.3241∗∗∗−0.3263∗∗∗(0.0122) (0.0122) (0.0122) (0.0122)(0.0123) (0.0123) (0.0123) (0.0122)
year 9−0.3439∗∗∗−0.3437∗∗∗−0.3442∗∗∗−0.3454∗∗∗ −0.3418∗∗∗−0.3415∗∗∗−0.3423∗∗∗−0.3444∗∗∗(0.0119) (0.0119) (0.0119) (0.0119)(0.0119) (0.0119) (0.0119) (0.0119)
year 10−0.3476∗∗∗−0.3474∗∗∗−0.3479∗∗∗−0.3492∗∗∗ −0.3456∗∗∗−0.3451∗∗∗−0.3459∗∗∗−0.3480∗∗∗39

(Continued from previous page)

year 11year 12year 13
year 14
year 15
year 16year 17year 18
year 19
year 20year 21year 22
Constant
Log-likelihood
Observations

Productivity
Productivity Growth
Regressions
Regression
OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12 OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(0.0115) (0.0115) (0.0115) (0.0115)(0.0115) (0.0115) (0.0115) (0.0115)−0.3183∗∗∗−0.3180∗∗∗−0.3185∗∗∗−0.3195∗∗∗ −0.3161∗∗∗−0.3157∗∗∗−0.3165∗∗∗−0.3184∗∗∗(0.0111) (0.0111) (0.0111) (0.0111)(0.0110) (0.0110) (0.0110) (0.0111)−0.3335∗∗∗−0.3331∗∗∗−0.3335∗∗∗−0.3345∗∗∗ −0.3312∗∗∗−0.3307∗∗∗−0.3316∗∗∗−0.3334∗∗∗(0.0109) (0.0109) (0.0109) (0.0109)(0.0109) (0.0109) (0.0109) (0.0109)−0.3144∗∗∗−0.3139∗∗∗−0.3143∗∗∗−0.3150∗∗∗ −0.3126∗∗∗−0.3119∗∗∗−0.3126∗∗∗−0.3140∗∗∗(0.0107) (0.0107) (0.0107) (0.0107)(0.0107) (0.0107) (0.0107) (0.0107)−0.2825∗∗∗−0.2820∗∗∗−0.2825∗∗∗−0.2831∗∗∗ −0.2809∗∗∗−0.2799∗∗∗−0.2807∗∗∗−0.2820∗∗∗(0.0104) (0.0104) (0.0104) (0.0104)(0.0104) (0.0104) (0.0104) (0.0104)−0.2626∗∗∗−0.2620∗∗∗−0.2624∗∗∗−0.2631∗∗∗ −0.2607∗∗∗−0.2597∗∗∗−0.2605∗∗∗−0.2618∗∗∗(0.0102) (0.0102) (0.0102) (0.0102)(0.0102) (0.0102) (0.0102) (0.0102)−0.2540∗∗∗−0.2535∗∗∗−0.2538∗∗∗−0.2543∗∗∗ −0.2522∗∗∗−0.2513∗∗∗−0.2520∗∗∗−0.2533∗∗∗(0.0101) (0.0101) (0.0101) (0.0101)(0.0101) (0.0101) (0.0101) (0.0101)−0.2502∗∗∗−0.2498∗∗∗−0.2500∗∗∗−0.2504∗∗∗ −0.2488∗∗∗−0.2477∗∗∗−0.2481∗∗∗−0.2494∗∗∗(0.0098) (0.0098) (0.0098) (0.0098)(0.0098) (0.0098) (0.0098) (0.0098)−0.2361∗∗∗−0.2359∗∗∗−0.2360∗∗∗−0.2362∗∗∗ −0.2347∗∗∗−0.2338∗∗∗−0.2340∗∗∗−0.2349∗∗∗(0.0097) (0.0097) (0.0097) (0.0097)(0.0097) (0.0097) (0.0097) (0.0097)−0.2074∗∗∗−0.2072∗∗∗−0.2074∗∗∗−0.2070∗∗∗ −0.2053∗∗∗−0.2048∗∗∗−0.2054∗∗∗−0.2060∗∗∗(0.0094) (0.0094) (0.0094) (0.0094)(0.0094) (0.0094) (0.0094) (0.0094)−0.1575∗∗∗−0.1573∗∗∗−0.1573∗∗∗−0.1564∗∗∗ −0.1551∗∗∗−0.1550∗∗∗−0.1553∗∗∗−0.1552∗∗∗(0.0091) (0.0091) (0.0091) (0.0091)(0.0091) (0.0091) (0.0091) (0.0091)−0.0033 −0.0037 −0.0036 −0.0022−0.0007 −0.0008 −0.0012 −0.0005(0.0094) (0.0094) (0.0094) (0.0094)(0.0094) (0.0094) (0.0094) (0.0094)−0.0465∗∗∗−0.0471∗∗∗−0.0469∗∗∗−0.0455∗∗∗ −0.0446∗∗∗−0.0455∗∗∗−0.0455∗∗∗−0.0447∗∗∗(0.0091) (0.0091) (0.0091) (0.0091)(0.0091) (0.0091) (0.0091) (0.0091)−0.3314∗∗∗−0.3320∗∗∗−0.3313∗∗∗−0.3286∗∗∗ −0.3402∗∗∗−0.3444∗∗∗−0.3408∗∗∗−0.3350∗∗∗(0.0124) (0.0124) (0.0124) (0.0124)(0.0130) (0.0128) (0.0127) (0.0126)-355398.98 -355393.13 -355395.49 -355410.54 -355355.05 -355346.29 -355358.24 -355384.75
286954286954286954286954286954286954286954286954N OTE .– Dependent variable is the number of patents applied by inventor. Collaborations from limited intervals before the job switch
are considered. The Intervals are within 36 months, 24 months, and 12 months before the job switch. Robust standard errors are inparentheses. ∗ p < .1. ∗ ∗ p < .05. ∗ ∗ ∗ p < .01.

40

Table C. 2: Fixed-Effect Regression: All Estimation Results

Network referral dummy: ρ

Productivity
Productivity Growth
Regressions
Regression
OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12 OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.1023∗∗∗ 0.1270∗∗∗ 0.1551∗∗∗ 0.1806∗∗∗(0.0104) (0.0117) (0.0129) (0.0169)

Interaction terms of networkand tenure dummies: ρkyear 10.0801∗∗∗ 0.0935∗∗∗ 0.1195∗∗∗ 0.1368∗∗∗(0.0120) (0.0138) (0.0154) (0.0208)
0.1323∗∗∗ 0.1844∗∗∗ 0.2133∗∗∗ 0.2599∗∗∗(0.0172) (0.0199) (0.0220) (0.0287)
0.2166∗∗∗ 0.2640∗∗∗ 0.2865∗∗∗ 0.3101∗∗∗(0.0186) (0.0221) (0.0244) (0.0322)
0.1589∗∗∗ 0.1794∗∗∗ 0.1980∗∗∗ 0.2250∗∗∗(0.0226) (0.0279) (0.0312) (0.0417)
0.1026∗∗∗ 0.1120∗∗∗ 0.1329∗∗∗ 0.0793(0.0271) (0.0338) (0.0384) (0.0525)
0.0907∗∗∗ 0.1111∗∗∗ 0.1238∗∗∗ 0.1755∗∗∗(0.0320) (0.0406) (0.0468) (0.0613)
0.06190.03850.04260.0224(0.0377) (0.0488) (0.0569) (0.0784)
0.05710.08650.1331∗∗ 0.1784∗∗(0.0436) (0.0541) (0.0617) (0.0816)−0.1158∗∗ −0.0836
0.01670.0399(0.0549) (0.0678) (0.0751) (0.1014)−0.1161∗ −0.0655 −0.0398 −0.1252(0.0631) (0.0759) (0.0865) (0.1262)−0.0867 −0.1216 −0.0300
0.1424(0.0717) (0.0875) (0.0976) (0.1320)−0.2427∗∗∗−0.2580∗∗ −0.2304∗ −0.0964(0.0906) (0.1082) (0.1282) (0.1786)−0.2978∗∗∗−0.2851∗∗ −0.1385 −0.1493(0.1061) (0.1253) (0.1407) (0.2075)−0.2420∗ −0.2887∗ −0.3027∗ −0.1851(0.1322) (0.1498) (0.1726) (0.2508)−0.3046∗∗ −0.3629∗∗ −0.2603 −0.0940(0.1517) (0.1751) (0.1893) (0.2526)
0.1332 −0.3750∗ −0.4957∗∗ −0.2807(0.1419) (0.2029) (0.2362) (0.3115)−0.7175∗∗∗−0.6825∗∗ −0.8391∗∗∗−0.6531(0.2489) (0.2740) (0.3228) (0.4176)−0.1575 −0.1700
0.0813 −0.2941(0.2461) (0.2608) (0.2713) (0.4605)−0.5998 −0.6160 −0.6960 −0.5140(0.4584) (0.4586) (0.5896) (0.5918)
0.27640.26200.30170.3563(0.4309) (0.4310) (0.4314) (0.4731)−0.5669 −0.5803 −0.5392 −0.4971

year 2
year 3
year 4
year 5year 6
year 7
year 8year 9year 10year 11year 12year 13
year 14
year 15
year 16year 17year 18
year 19
year 20year 2141

(Continued from previous page)

Productivity
Regressions
OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12
(1)
(2)
(3)
(4)

Productivity Growth
Regression
OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12
(5)
(6)
(7)
(8)
(1.0226) (1.0226) (1.0228) (1.0235)
year 22−0.0654 −0.0771 −0.0358
0.0072(0.7390) (0.7390) (0.7393) (0.7402)
Referee’s productivity
0.0012∗∗∗ 0.0014∗∗∗ 0.0014∗∗∗ 0.0017∗∗∗ 0.0011∗∗∗ 0.0014∗∗∗ 0.0014∗∗∗ 0.0017∗∗∗(0.0002) (0.0002) (0.0002) (0.0002)(0.0002) (0.0002) (0.0002) (0.0002)
Past productivity−0.0394∗∗∗−0.0395∗∗∗−0.0395∗∗∗−0.0392∗∗∗ −0.0395∗∗∗−0.0396∗∗∗−0.0396∗∗∗−0.0392∗∗∗(0.0008) (0.0008) (0.0008) (0.0008)(0.0008) (0.0008) (0.0008) (0.0008)
Total research experience
0.0159∗∗∗ 0.0167∗∗∗ 0.0170∗∗∗ 0.0177∗∗∗ 0.0153∗∗∗ 0.0162∗∗∗ 0.0167∗∗∗ 0.0175∗∗∗(0.0015) (0.0015) (0.0015) (0.0015)(0.0015) (0.0015) (0.0015) (0.0015)(Total research experience)20.0011∗∗∗ 0.0010∗∗∗ 0.0010∗∗∗ 0.0010∗∗∗ 0.0011∗∗∗ 0.0011∗∗∗ 0.0011∗∗∗ 0.0010∗∗∗(0.0001) (0.0001) (0.0001) (0.0001)(0.0001) (0.0001) (0.0001) (0.0001)
Within-firm research experience
0.0062∗∗ 0.0053∗
0.0052∗0.00480.0101∗∗∗ 0.0086∗∗∗ 0.0076∗∗ 0.0066∗∗(0.0031) (0.0031) (0.0031) (0.0031)(0.0033) (0.0032) (0.0032) (0.0031)(Within-firm research experience)2−0.0007∗∗∗−0.0007∗∗∗−0.0007∗∗∗−0.0006∗∗∗ −0.0008∗∗∗−0.0007∗∗∗−0.0007∗∗∗−0.0007∗∗∗(0.0002) (0.0002) (0.0002) (0.0002)(0.0002) (0.0002) (0.0002) (0.0002)
The first tenure year dummy
0.5396∗∗∗ 0.5394∗∗∗ 0.5396∗∗∗ 0.5390∗∗∗ 0.5569∗∗∗ 0.5548∗∗∗ 0.5515∗∗∗ 0.5465∗∗∗(0.0071) (0.0071) (0.0071) (0.0071)(0.0076) (0.0074) (0.0073) (0.0072)
Firm’s productivity
0.3045∗∗∗ 0.3061∗∗∗ 0.3072∗∗∗ 0.3074∗∗∗ 0.3024∗∗∗ 0.3036∗∗∗ 0.3055∗∗∗ 0.3054∗∗∗(0.0172) (0.0173) (0.0173) (0.0172)(0.0172) (0.0172) (0.0172) (0.0172)
Field dummies:
Chemical
0.1971∗∗∗ 0.1970∗∗∗ 0.1973∗∗∗ 0.1972∗∗∗ 0.1983∗∗∗ 0.1979∗∗∗ 0.1980∗∗∗ 0.1978∗∗∗(0.0101) (0.0101) (0.0101) (0.0101)(0.0101) (0.0101) (0.0101) (0.0101)
Comp.& Comm.
0.1630∗∗∗ 0.1635∗∗∗ 0.1634∗∗∗ 0.1639∗∗∗ 0.1636∗∗∗ 0.1639∗∗∗ 0.1635∗∗∗ 0.1639∗∗∗(0.0122) (0.0122) (0.0122) (0.0122)(0.0122) (0.0122) (0.0122) (0.0122)
Drugs & Medical
0.1691∗∗∗ 0.1693∗∗∗ 0.1692∗∗∗ 0.1687∗∗∗ 0.1705∗∗∗ 0.1703∗∗∗ 0.1698∗∗∗ 0.1692∗∗∗(0.0155) (0.0155) (0.0155) (0.0155)(0.0155) (0.0155) (0.0155) (0.0155)
Elec. & Electronics
0.1812∗∗∗ 0.1813∗∗∗ 0.1811∗∗∗ 0.1817∗∗∗ 0.1819∗∗∗ 0.1818∗∗∗ 0.1812∗∗∗ 0.1820∗∗∗(0.0099) (0.0099) (0.0099) (0.0099)(0.0099) (0.0099) (0.0099) (0.0099)
Mechanical
0.1684∗∗∗ 0.1691∗∗∗ 0.1702∗∗∗ 0.1700∗∗∗ 0.1698∗∗∗ 0.1706∗∗∗ 0.1714∗∗∗ 0.1706∗∗∗(0.0089) (0.0089) (0.0089) (0.0089)(0.0089) (0.0089) (0.0089) (0.0089)
Others0.1802∗∗∗ 0.1802∗∗∗ 0.1810∗∗∗ 0.1803∗∗∗ 0.1811∗∗∗ 0.1808∗∗∗ 0.1814∗∗∗ 0.1806∗∗∗(0.0092) (0.0092) (0.0092) (0.0092)(0.0092) (0.0092) (0.0092) (0.0092)
Annual dummies
year 20.01040.01010.01000.00960.01640.01540.01420.0121(0.0163) (0.0163) (0.0163) (0.0163)(0.0163) (0.0163) (0.0163) (0.0163)
year 3−0.0412∗∗ −0.0420∗∗∗−0.0421∗∗∗−0.0422∗∗∗ −0.0345∗∗ −0.0362∗∗ −0.0377∗∗ −0.0399∗∗(0.0160) (0.0160) (0.0160) (0.0160)(0.0160) (0.0160) (0.0160) (0.0160)
year 4−0.1031∗∗∗−0.1042∗∗∗−0.1046∗∗∗−0.1047∗∗∗ −0.0972∗∗∗−0.0993∗∗∗−0.1008∗∗∗−0.1025∗∗∗(0.0157) (0.0157) (0.0157) (0.0157)(0.0158) (0.0157) (0.0157) (0.0157)
year 5−0.1327∗∗∗−0.1340∗∗∗−0.1342∗∗∗−0.1346∗∗∗ −0.1278∗∗∗−0.1300∗∗∗−0.1310∗∗∗−0.1327∗∗∗(0.0153) (0.0153) (0.0153) (0.0153)(0.0153) (0.0153) (0.0153) (0.0153)
year 6−0.1733∗∗∗−0.1746∗∗∗−0.1748∗∗∗−0.1753∗∗∗ −0.1692∗∗∗−0.1714∗∗∗−0.1721∗∗∗−0.1736∗∗∗(0.0149) (0.0149) (0.0149) (0.0149)(0.0150) (0.0150) (0.0150) (0.0149)
year 7−0.2157∗∗∗−0.2170∗∗∗−0.2172∗∗∗−0.2182∗∗∗ −0.2123∗∗∗−0.2141∗∗∗−0.2148∗∗∗−0.2167∗∗∗(0.0147) (0.0147) (0.0147) (0.0147)(0.0147) (0.0147) (0.0147) (0.0147)
year 8−0.2734∗∗∗−0.2747∗∗∗−0.2747∗∗∗−0.2761∗∗∗ −0.2709∗∗∗−0.2725∗∗∗−0.2727∗∗∗−0.2749∗∗∗(0.0144) (0.0144) (0.0144) (0.0144)(0.0145) (0.0144) (0.0144) (0.0144)
year 9−0.3139∗∗∗−0.3153∗∗∗−0.3156∗∗∗−0.3171∗∗∗ −0.3120∗∗∗−0.3134∗∗∗−0.3138∗∗∗−0.3160∗∗∗(0.0142) (0.0142) (0.0142) (0.0142)(0.0142) (0.0142) (0.0142) (0.0142)
year 10−0.3370∗∗∗−0.3382∗∗∗−0.3385∗∗∗−0.3403∗∗∗ −0.3349∗∗∗−0.3362∗∗∗−0.3364∗∗∗−0.3388∗∗∗42

(Continued from previous page)

year 11year 12year 13
year 14
year 15
year 16year 17year 18
year 19
year 20year 21year 22
Log-likelihood
Observations

Productivity
Productivity Growth
Regressions
Regression
OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12 OVERALL W ITHIN 36 W ITHIN 24 W ITHIN 12
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(0.0139) (0.0139) (0.0139) (0.0139)(0.0140) (0.0139) (0.0139) (0.0139)−0.3133∗∗∗−0.3143∗∗∗−0.3151∗∗∗−0.3167∗∗∗ −0.3114∗∗∗−0.3124∗∗∗−0.3129∗∗∗−0.3151∗∗∗(0.0134) (0.0134) (0.0134) (0.0134)(0.0135) (0.0134) (0.0134) (0.0134)−0.3353∗∗∗−0.3361∗∗∗−0.3367∗∗∗−0.3386∗∗∗ −0.3334∗∗∗−0.3341∗∗∗−0.3348∗∗∗−0.3374∗∗∗(0.0132) (0.0132) (0.0132) (0.0132)(0.0132) (0.0132) (0.0132) (0.0132)−0.3378∗∗∗−0.3385∗∗∗−0.3390∗∗∗−0.3408∗∗∗ −0.3368∗∗∗−0.3370∗∗∗−0.3375∗∗∗−0.3396∗∗∗(0.0127) (0.0127) (0.0127) (0.0127)(0.0127) (0.0127) (0.0127) (0.0127)−0.3164∗∗∗−0.3163∗∗∗−0.3169∗∗∗−0.3191∗∗∗ −0.3153∗∗∗−0.3144∗∗∗−0.3150∗∗∗−0.3176∗∗∗(0.0123) (0.0123) (0.0123) (0.0123)(0.0124) (0.0124) (0.0124) (0.0123)−0.3081∗∗∗−0.3076∗∗∗−0.3083∗∗∗−0.3108∗∗∗ −0.3067∗∗∗−0.3054∗∗∗−0.3062∗∗∗−0.3092∗∗∗(0.0121) (0.0121) (0.0121) (0.0121)(0.0121) (0.0121) (0.0121) (0.0121)−0.3227∗∗∗−0.3221∗∗∗−0.3226∗∗∗−0.3252∗∗∗ −0.3214∗∗∗−0.3197∗∗∗−0.3203∗∗∗−0.3236∗∗∗(0.0120) (0.0120) (0.0120) (0.0120)(0.0120) (0.0120) (0.0120) (0.0120)−0.3360∗∗∗−0.3348∗∗∗−0.3356∗∗∗−0.3382∗∗∗ −0.3348∗∗∗−0.3324∗∗∗−0.3331∗∗∗−0.3367∗∗∗(0.0120) (0.0120) (0.0120) (0.0120)(0.0120) (0.0120) (0.0120) (0.0120)−0.3353∗∗∗−0.3337∗∗∗−0.3344∗∗∗−0.3375∗∗∗ −0.3340∗∗∗−0.3310∗∗∗−0.3316∗∗∗−0.3353∗∗∗(0.0119) (0.0119) (0.0119) (0.0119)(0.0119) (0.0119) (0.0119) (0.0119)−0.3138∗∗∗−0.3118∗∗∗−0.3126∗∗∗−0.3149∗∗∗ −0.3119∗∗∗−0.3090∗∗∗−0.3098∗∗∗−0.3133∗∗∗(0.0118) (0.0118) (0.0118) (0.0118)(0.0118) (0.0118) (0.0118) (0.0118)−0.2711∗∗∗−0.2689∗∗∗−0.2692∗∗∗−0.2708∗∗∗ −0.2688∗∗∗−0.2662∗∗∗−0.2665∗∗∗−0.2689∗∗∗(0.0117) (0.0117) (0.0117) (0.0117)(0.0117) (0.0117) (0.0117) (0.0117)−0.1024∗∗∗−0.1011∗∗∗−0.1017∗∗∗−0.1026∗∗∗ −0.0999∗∗∗−0.0980∗∗∗−0.0986∗∗∗−0.0998∗∗∗(0.0115) (0.0115) (0.0115) (0.0115)(0.0115) (0.0115) (0.0115) (0.0115)−0.1516∗∗∗−0.1510∗∗∗−0.1515∗∗∗−0.1516∗∗∗ −0.1505∗∗∗−0.1493∗∗∗−0.1497∗∗∗−0.1503∗∗∗(0.0122) (0.0122) (0.0122) (0.0122)(0.0122) (0.0122) (0.0122) (0.0122)-236433.22 -236423.18 -236410.06 -236425.31 -236363.28 -236356.18 -236358.39 -236393.13
286954286954286954286954286954286954286954286954N OTE .– Dependent variable is the number of patents applied by inventor. Collaborations from limited intervals before the job switch
are considered. The Intervals are within 36 months, 24 months, and 12 months before the job switch. ∗ p < .1. ∗ ∗ p < .05.
∗ ∗ ∗ p < .01.

43

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