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Stars and their Constellations: Great person or Great team?

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Stars and their Constellations: Great person or great team?

Denisa Mindruta

HEC-Paris

Janet Bercovitz University of Illinois at Urbana-Champaign

Maryann Feldman

University of North Carolina

This version: January, 2016 This paper examines star scientists and team performance in the context of academic entrepreneurship.

We employ a matching model of value creation to estimate complementarities underlying team formation.

Through an extension of the matching model, we determine the contribution intervals of the stars and

their constellations by creating counterfactual collaborations where stars, and respectively constellations,

are reassigned to the best available partner under a scenario in which their actual partner is absent from

the market. The value-added contribution of a party is determined by the “value-loss” created in the

market by that party’s absence. While results are, on average, supportive of a star-to-team spillover effect,

our analysis raises the salience of team contribution as stars with a ‘net contribution’ are few.

Work in progress – Comments Welcomed! Please do not cite or distribute without permission


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Introduction

In the literature on scientific research teams, “Star” scientists are often emphasized as an

important, if not the most important, form of human capital. In the academic setting, these individuals,

who both publish more than their peers and produce papers that have greater impact, have also been found

to be at the forefront of commercialization efforts – active in invention disclosure, patenting, licensing,

and start-ups (Zucker et. al, 1998). Often overlooked when assessing the technical and entrepreneurial

contributions of this elite group of scientists is the larger scientific community in which they are

embedded and the individuals with whom they work. Scientists, particularly those in the medical and life

sciences fields, rarely work alone. Rather, research activities are conducted in departmental laboratory

groups, as part of within university research teams, or in cross-institution collaborations. As documented

by Adams et al. (2005) the average number of authors per paper in the medical field is above four and

trending higher. Commercialization activity also appears to be a team endeavor as the bulk of invention

disclosures, the first step in the university-industry commercialization process, involves multiple

inventors. At two major research institutions, invention disclosure has been documented to be a team

effort more than 60% of the time (Bercovitz and Feldman, 2011).

While interest in “Star” scientists has been high, more research that places the star in the team

context is needed to understand not only how these individuals influence the research teams in which they

participate, but also how they, themselves, are influenced in their productivity by the other team

members.

In early work on the social structure of science, Merton (1968) draws attention to the Matthew

Effect in science, noting that prominent scientists tend to receive disproportionate recognition and

resources for their contributions to science as compared to lessor known scientists contributing

comparable advances. In simple terms, there is a “rich get richer” dynamic as initial advantages beget

further advantages (Cole & Cole, 1973; Simcoe & Waguespack, 2011). This perspective suggests that,

considered in a team context, stars may be performing at a high-level in part because of their ability to

attract stronger collaborators (Allison & Stewart, 1974). The more recent studies that acknowledge the


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team context have generally focused on star to team spillovers, rather than team to star flows. These

studies highlight the unique knowledge assets stars bring to the team and investigate the effect of star

scientists on the subsequent productivity of their collaborators (e.g., Azoulay, Zivin, and Wang, 2010;

Oettl, 2012).

The central question is what drives the superior performance of Star-Teams? Is it a function of a

Matthew effect (stars are advantaged and able to assemble stronger teams) or is it a function of the value-

added effect (stars provide spillovers to their team members which raises performance)? To date, no one

study has looked deeply at the relative influence of these two effects. One particular challenge in

estimating these effects stems from the team-formation process. Because collaboration is not random,

disentangling the value-added of the stars versus that of the teams needs to take into account how they

initially match. That is, the challenge is parsing star selection and treatment effects on performance, given

endogenous team formation. Our study aims to fill this gap. We examine this issue in the context of

academic research teams that disclose inventions. Delving in to the research activities at a prominent (R1)

university with a top-rated medical school, we have complied a dataset of all 623 research teams that

disclosed an invention during a 11 year period. Teams are comprised of a principal investigator (PI) who

may have ‘star’ status or not, and the ‘constellation’ of other researchers working with this individual.

Following prior work (e.g. Azoulay et. al., 2010), we defined as ‘stars’ the 40 scientists falling in the top

five percent of the distribution of citations received by the 1024 individuals in our database in a time

window of five years prior to the collaboration year.

We develop a matching model of PI-constellation team formation and we use the estimates of the

matching equation to gauge the value-added contribution of each party. The novelty of our approach

consists in creating counterfactual collaborations where stars, and respectively constellations, are

reassigned to the best available partner under a scenario in which their actual partner is absent from the

market. The value-added contribution of a party is determined by the “value-loss” created in the market

by that party’s absence. This is what MacDonald and Ryall (2004) define as “marginal product” and is an

integral part of the Brandenburger and Stuart (1996) value-creation value-capture approach. We find that


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on average, the added-value of stars surpasses that of the teams in most collaborations. However when we

compare the range of stars’ and teams’ contribution (that is, the interval spanning from the lower to the

higher bound of their contribution) we find that there are only nine cases where the lower bound of stars’

contribution is higher than the upper bound of their teams’ contribution. Our analysis thus raises the

salience of team contribution given that the stars with a ‘net contribution’ are few.

The paper proceeds as follows. In Section 2, we review the relevant literature on team science

and star scientists to set the foundation for our investigations of complementarities in team-PI matches as

well as our exploration of the relative value-add by stars (PIs) and their constellations (teams). Based on

this theoretical framework, in Section 3, we present our methodological design. Section 4 introduces our

data and variables while also further explicating our empirical method. We summarize and discuss our

empirical results in Section 5 and offer concluding thoughts in Section 6.

Literature Review

The Role of Teams in Science: Knowledge Production and Innovation

Recent empirical studies have detailed two central developments in the organization and

performance of scientific research. First, research activities are increasingly shifting from being an

individual effort to a team-based endeavor. In 2005, over 80% of science and engineering publications

and close to two-thirds of all patents were products of multiple authors or inventors (Jones, 2011).

Further, the size of scientific research teams has been growing steadily with the number of authors per

paper and/or inventors per patent rising at rates of 15-20% per decade since the mid-1950s (Adams, et al.

2005; Wutchy et al. 2007). Second, the evidence suggests a performance differential between team-based

efforts and individual efforts, with teams increasingly emerging as the source of higher-impact work

(Singh & Fleming, 2010). Team-authored papers are both more highly cited on average and more likely

to reach the higher echelons of the citation distribution as compared to solo-authored papers (Bikard,

Murray & Gans, 2015; Jones, 2009; Jones, 2011; Wutchy, et al. 2007). In sum, teams have become

pervasive and essential players in scientific knowledge production and innovation.


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Given the growing importance of teams, understanding how team composition influences team

performance merits attention. Numerous factors have been discussed in the literature to date. These

factors fall within four main categories: (1) Size-related, (2) Knowledge-related; (3) Experience-related,

and (4) Quality-related. We review each of these in turn.

Size-Related

Team size has been argued to have both positive and negative influences on team performance.

On the positive side, larger teams can signal the presence of greater resources. The sheer fact that a large

team has been assembled for a research effort reflects a substantial commitment in personnel, facilities,

and research materials. Further, as each individual team member brings their own set of human and

social capital to the collaboration, larger teams can benefit from greater aggregate capital levels as well as

the subsequent effort of a greater number of individuals (Reagans & Zuckerman, 2001). On the negative

side, team performance may suffer given the increasing coordination challenges, free-riding hazards, and

credit-sharing concerns that accompany expansion of team membership (Holmstron, 1982; Jin, Jones, Lu,

& Uzzi, 2013). Size alone, however, is a blunt indicator of team capabilities and/or internal dynamics.

Understanding the level of benefits or costs accruing to a research team of any particular size requires

delving into the unique knowledge-related and quality-related attributes of individual team members as

well as the experience-based relationships between team members.

Knowledge-Related

One oft-cited benefit of teams in knowledge production is the potential to support individual

specialization, yet derive productivity gains through teamwork that integrates the specialized knowledge

of the individual team members (Adams et al., 2005). The advantage of such division of labor is typically

argued to accrue from success in managing the rapidly expanding “burden of knowledge” needed to

operate at the technological frontier in an innovative field (Jones, 2009). Through collaboration, a

scientific team can both benefit from, as well as overcome the limitations of, the deep yet narrow

knowledge of individual members (Jones, 2011; Bikard et. al., 2015). Relatedly, teams can bring together

scientists holding diverse (across-technological fields) knowledge leading to both search and


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combinational advantages. Specifically, scientists grounded in different disciplines will approach

research challenges from different perspectives and are likely to search for solutions in different

technological domains. This broader search can give rise to novel insights and new research trajectories

(Reagans, Zuckerman & McKevily, 2004). In addition, the diverse knowledge of team members allows

for greater experimentation and the generation of a greater set of domain-based combinational

alternatives. It is these unique combinations that underlie discovery and spawn innovation (Schumpeter,

1939; Taylor & Greve, 2006; Fleming, Mingo & Chen, 2007). Team members may also be differentiated

by stage of research focus –a focus on basic research versus an applied, or translational, research focus.

Such variation in research focus may augment team performance by simultaneously raising the salience of

invention via scientific breakthroughs and innovation via needs-directed clinical refinement (Dosi, 1982).

Beyond scientific disciplines and stage of research focus, professional age (or cohort

membership) can contribute to the knowledge diversity of a team. There is significant evidence of a

“vintage effect” in science, where academics trained in different periods anchor to different reference

points in the field and hold different research, innovation, and commercialization norms. These cohort

differences can simultaneously influence question selection, investigative process, and scientific

productivity (Levin & Stephan, 1991; Bercovitz & Feldman, 2008). Similar to discipline-based diversity,

cohort-based diversity is expected to increase the range of search and the variation of knowledge domains

drawn upon leading to the development of more innovative solutions (Guimera, Uzzi, Spiro, & Amaral,

2005).

Experience-Related

While knowledge-related diversity may catalyze innovation, such diversity (and the subsequent

need for integration) can also raise communication and coordination challenges for the team (Zenger &

Lawrence, 1989; Pelled, Eisenhardt, & Xin, 1999; Reagans & Zuckerman, 2001). Team members

grounded in distant or non-overlapping areas of expertise may use discipline-specific languages and

methods that are foreign to their collaborators. Such habits and practices can impede the development of

common understanding and hinder knowledge transfer (Simonin, 1999). Communication difficulties, in


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turn, can exacerbate coordination challenges as key interdependencies in the research process may not be

recognized, conveyed, or addressed in a timely and efficient manner (Porac, Wade, Fischer, Brown,

Kanfer, & Bowker, 2004; Cummings and Kiesler, 2005). Taken together, such communication and

coordination hurdles can slow, or even derail, scientific advancement.

Experience – particularly the experience the team has had working together previously – is held

to mitigate such diversity-related communication and coordination problems. Through past interactions,

team members gain familiarity with the field-specific scientific jargon, as well as the training-ingrained

perspectives of their cross-disciplinary peers. Via experience, research teams establish a baseline level of

common understanding that facilitates knowledge sharing and joint knowledge production. Moreover,

informational social influence, a by-product of familiarity, can engender a melding of research

perspectives and lead to a collective research identity within the team (Katz, 1982). In addition to better

communication, prior experience working together enables the team to map-out standardized practices

and develop routines. These routines can streamline the research process and effectively reduce the

coordination costs of leveraging the diverse knowledge that is distributed across the team (Taylor &

Greve, 2006).

A second type of experience – the career experience of individual team members – has also been

hypothesized to smooth coordination and communications challenges. Tenure as a researcher is

associated with the accumulation of skills. One important skill that can be developed over a research

career is general collaboration capabilities. Complementary to partner-specific collaboration capabilities,

general collaboration capabilities, can be accrued through experience working in multiple research teams

with numerous sets of colleagues (Hoang & Rothaermel, 2005). Development of communication

strategies to convey details about one’s own research topic to non-specialists or building an awareness of

the existence and relative effectiveness of alternative coordination mechanisms are two general

collaboration capabilities an individual academic may gain over time (Cummings and Kiesler, 2005).

Interesting, though the average career tenure of individuals on a team is generally associated with a

reduction in communication and coordination costs, variation (or range) of career experience of the team


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members appear to introduce additional costs related to credit-sharing and free-riding (Bikard, et al.,

2015).

Quality-Related

A final, and perhaps fundamental, attribute of teams believed to underlie team performance is

team member “quality”.1 Whether considered in terms of expertise or productivity, the overall capability

of a team is believed to be a product of the abilities of each of the individual team members. A collection

of high-performing researchers is more likely to coalesce into a high-performing team than is a collection

of low-performing researchers. Further, it is well-established that the inclusion on the team of a “star

scientist” – an individual whose past activities place him/her in the upper stratum of research

achievements as compared to their scientific peers –is linked to higher team-level performance (Zucker &

Darby, 1997; Zucker, Darby, Brewer, 2002; Bercovitz & Feldman, 2011) However, the mechanism by

which this “star-related” value is created continues to be debated in the literature with some authors

highlighting a star-based spillover effect with others emphasizing a team-related accumulated advantage

effect (Merton, 1968; Azoulay, et al, 2010; Higgins, Stephan, & Thursby, 2011; Oettl, 2012; Agrawal,

McHale, & Oettl, 2014). As this is a central puzzle we seek to illuminate in our study, we offer an

expanded discussion of this literature in the section below.

The Role of Star Scientists in the Context of a Team: Knowledge Production and Innovation

A ground-breaking treatise exploring the role star (or eminent) scientists play in the social

structure of science, “The Matthew Effect in Science,” was penned by RK Merton in 1968.2 This essay,

which remains central in current theoretical conversations, identifies two elements – one perceptual and

one tangible – that catalyze feedback loops resulting in accumulated advantage, a “Matthew Effect.” The

first, recognition, functions via status signals that disproportionally draw attention and give attribution to

1Though an imperfect measure of ability, past research achievements (proxied by publication counts and citation rates) shape scientific reputations and is often interpreted as a signal researcher quality (Stern, Dukerich & Zajac, 2014) 2 The article is based on a paper presentation given by Merton to the American Sociological Association in 1967.


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the work of prominent researchers as compared to their less prominent peers (Merton, 1968).3 The

second, resources, functions via adoption of distribution mechanisms that over-reward past achievements

in the allocation of scientific inputs for new projects.

There is a large body of literature studying status dynamics in market situations that has

explicated the mechanism and empirically supported the presence of a Matthew effect for organizations in

competitive arenas (e.g., Podolny, 1993, 1994, 2005; Podolny & Phillips, 1996). Similarly, a number of

authors have leveraged Merton’s early work to further investigate the role status plays in the distribution

of rewards in knowledge production and innovation. For example, Azoulay, Stuart, & Wang (2014)

exploit the “shock” of receiving a coveted appointment as investigators of the Howard Hughes Medical

Institute (HHMI) to assess the effect a positive status shift has on external perceptions of the value of an

awardee’s existing body of work. In-line with the theorized Matthew effect, these authors find a modest

post-award citation boost that is increasing under conditions of uncertainty about product (article) or

awardee (relatively low pre-award status) quality. In another study, Jin et al. (2013) uncover evidence of

a “reverse Matthew effect” where the negative fall-out from scientific misconduct (paper retraction

events) falls disproportionately upon the lower status co-authors. A third paper by Simcoe &

Waguespack, (2011), explores status-based dynamics in the context of open Internet standards

development. Taking advantage of a natural experiment, this study tracks how author visibility (via the

use or non-use of “et al.” conventions) influences proposal evaluation efforts and acceptance rates.

Analysis shows that when name-based status signals are obscured or unobservable, the attention levels

garnered, as well as the publication rates achieved, drop significantly for proposals authored by high-

status individuals as compared to proposals authored by low-status individuals. All of these three articles

were carefully designed to capture status effects independent of the concomitant tangible resource flows.

3 Inspired by the Gospel According to Saint Matthew which reads, “For unto every one that shall be given, and he shall have in abundance; but from him that hath not shall be taken away even that which he hath,” Merton (1968: 58) offers a simplified translation: “Put in less stately language, the Matthew effect consists in the accruing of greater increments of recognition for particular scientific contributions to scientists of considerable repute and the withholding of such recognition from scientists who have not yet made their mark.”


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However, status-associated resource flows are highly pertinent in relation to both the context and

questions of our study. As such, we turn now to this second, resource-based, feedback loop.

Merton (1968) explicitly recognizes a second pathway in which status moves beyond influencing

perceptions of quality to having an actual effect on quality levels through generating preferential access to

valuable scientific inputs. Simply put, prestige often attracts the resources needed to produce quality

outputs4. Key resources for academic knowledge production and innovation include research funding

(both governmental grants and industry research agreements), high-caliber graduate students, engaged,

stimulating colleagues, state-of-the-art laboratory facilities, cutting-edge equipment, and protected

research time. Several recent studies have found evidence of a prestige-related “resource attraction

effect.” For example, Agarwal, McHale & Oettl, (2014) document positive recruiting externalities

following star hires. Specifically, departments that add star scientists benefit from an ability to

subsequently induce other strong, high-quality researchers to join the department. In a similar vein,

Higgins, Stephan, & Thursby (2011) cite anecdotal evidence of the recruiting value of Scientific Advisory

Boards (SAB) populated with prominent researchers. In the biotechnology industry, such high-quality

boards are believed to play a key role in attracting high-potential early-career scientists to young firms.

Simultaneously, the star scientists who sit on these boards as well as those stars that are otherwise

affiliated with these new biotechnology firms also gain, in part due to the organizational resources these

stars were able to attract and redirect to augment their own research programs (Zucker & Darby, 1996;

Zucker et al., 2002). The presence of a Matthew effect in science has ramifications for team composition

and performance. Namely, if prestige attracts resources, then stars may be advantageously positioned to

attract, and then benefit from, stronger teams.

An alternative dynamic that could influence both team composition and team performance is that

of star-to-constellation spillovers. It is feasible that, in addition to the individual productivity of the star

scientist, the documented superior performance of research teams that include eminent scientists may be

4Merton (1968: 62) offers the following as an illustration of this effect: “Centers of demonstrated scientific excellence are allocated far larger resources than centers which have yet to make their mark. In turn, their prestige attracts a disproportionate share of the truly promising graduate students”


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driven by positive externalities generated by the star that rebound to the benefit of the team. The inclusion

of a high-productivity individual could increase the productivity of a team through several avenues: (1)

knowledge and skill transfers – a learning-by-engagement effect; (2) socialization processes by which

norms and values that govern significant research are conveyed and internalized, and/or (3) via star

supplied research funds and research materials. Significant support for such spillover effects has been

found in past studies. Both Azoulay et al. (2010) and Oettl (2012) take advantage of a rare, yet disruptive,

event – the unexpected death of star –scientists – to sidestep endogeneity issues and investigate how of

such losses affect the subsequent performance of co-authors. Identifying stars based on cumulative

scientific achievement, Azoulay et al. (2010) observe a 5-10% lasting decrease in co-author quality-

adjusted publications following the death of a superstar collaborator. Further, analyses that separately

consider the influence of academic achievements (citations) and the funding achievements (NIH grants)

of star scientists support the conclusion that the performance declines suffered by surviving co-authors are

generally not attributable to funding disruptions but rather are driven by “the loss of irreplaceable source

of ideas” (Azoulay, et al. 2010: 552). Oettl (2012) broadens the definition of stars to include both

productivity and helpfulness and finds that the output quality of surviving co-authors decrease between

14-16% when a star collaborator dies as compared to when a non-star collaborator dies. Interestingly, the

majority of the star-specific peer effect appears to be attributable to the star helpfulness rather than star

productivity. Again the loss of knowledge-based spillovers, (captured by conceptual helpfulness – the

provision of intellectual feedback) is found to have much stronger impact than the loss of resource-based

spillovers (captured by material) helpfulness

Perhaps not surprisingly, evidence for both star-based spillover effects and team-related

accumulated advantage effects has been found in previous studies of star-team performance. The open

question, however, is one of relative contribution. There is growing agreement that, with respect to

knowledge production and innovation teams, the whole is generally greater than the sum of the parts.

Yet, our ability to design effective teams remains limited given significant uncertainty as to what degree,

and in what specific circumstances, the different elements (star or constellation) contribute to


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performance outcomes. This gap in our understanding rests, to some extent, on limited methodological

options available to address team formation and team performance endogeneity issues. We turn to that

challenge now.

Methodological design

The challenge in estimating the separate contribution of the principal investigator (PI) and the rest

of the team (constellation) to the joint output is the endogeneity of team formation. Principal investigators

and teams mutually select for collaboration under the expectation that certain partners will lead to higher

chances of success. More often than not, it is difficult for econometricians to account for all unobserved

factors leading to team formation (or to measure them perfectly), an aspect that leads to biased estimates

in standard regressions. To tackle this issue, researchers need to either employ appropriate econometric

methods (e.g. instrumental variables) or create an experimental design that allows for the manipulation of

the team formation process. However, both these approaches have their limitations. Good instruments are

difficult to find and experimental manipulation brings clarity at the expense of richness.

We thus approach this endogeneity problem from a different angle. Starting from the premise that

teams are constructed via a matching process, we ask in which circumstances the penalty for mismatching

is higher: when star-PIs work with other (potentially, non-star) teams than those with whom they initially

matched or when star-teams work with other (potentially, non-star) PIs than those with whom they

initially matched. Following Fox (2010) and Mindruta (2013), we extend an empirical matching model to

examine whether mismatching is more advantageous or detrimental to the star or the constellation. This

approach builds on the formal properties of cooperative game theory models discussed by MacDonald

and Ryall (2004) and Brandenburger and Stuart (1996). Specifically, we calculate the minimum and the

maximum of the added-value of an agent (scientist, respectively, star-team) in the market.5 The intuition

behind this approach is that in order to estimate an agent’s contribution to a relationship, one needs to 5 In a more general context, MacDonald and Ryall (2004) refer to the minimum as the “minimum residual” and the maximum as the “marginal product”, or the incremental value that an agent adds to the strategic interaction (MacDonald and Ryall, 2004: 1326).


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build counterfactual scenarios comparing the aggregate value produced in a given market with the agent

present in the market and with the agent absent from the market. The approach we propose here creates

these counterfactual scenarios by removing each star (respectively, each team) one at a time from the

market and then rematching the remaining participants in an optimal way, that is, by maximizing the

aggregate value created by all matches in a market (Shapley and Shubik, 1971).

Data and Variables

Sample

Our sample consists of the entire set of discoveries disclosed by academic scientists at one

prominent (R1) U.S. research university with a renowned Medical School. We have detailed data on the

composition and history of 623 invention teams from this university for years 1988 to 1999. These teams

constitute the entire population of teams that have disclosed an invention with the Technology Transfer

Office. Team formation in the academic research context is internally managed rather than externally

assigned or determined. Broadly, the PI sets-forth a research agenda, solicits funds to pursue this agenda,

and then self-organizes teams – either within his lab group or across lab groups – to explore specific

research questions. Team formation is two-sided selection process. The PI seeks to draw skilled students

and colleagues to his lab and projects, while these same individuals evaluate multiple opportunities

seeking to align themselves with most promising/interesting projects and investigators. As new teams are

formed around specific research questions, it is not unusual to see a particular PI concurrently working

with multiple unique teams.

Borrowing the operational definition of star scientist from Azoulay et. al. (2010), we defined a

group of highly productive scientists in terms of publications and external recognition through citations.

Specifically, we identified as “stars” the scientists in the top 5% of the distribution of citations received

by individuals in our sample during a five-year window prior to the collaboration year. The distribution of

citations takes into account the differences in citation propensity across fields by grouping scientists in

three broad areas – basic life sciences, clinical life sciences, and engineering & physical sciences –


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according to their departmental affiliation. The invention teams involve 1024 individual scientists

including the identified subset of 40 “star scientists”. We supplemented the original information on team

discoveries with publication records from ISI/Web of Knowledge, and with hand-collected information

on scientists’ vitae, bios and employment records.

Principal Investigator Attributes

We focused on two attributes of the principal investigators: Research Impact and Knowledge

Diversity. We measured Research Impact, one representation of research quality, as the number of

citations received by a scientist during a five-year window prior to (and including) the collaboration year.

We focused on a limited time window prior to collaboration to capture the extent to which a scientist’s

research has received recent recognition by the academic community in the years preceding collaboration.

We included citations and articles published during the collaboration year to account for publication lag.

The maximum of this variable in the sample is 19,483 citations and 40% of the individuals have more

than 1000 citations. To make the range of this variable more compatible with the other variables in the

model, we scaled it in the analysis by dividing it by 1000.

We measured Knowledge Diversity as the inverse of the Herfindahl index of publication

specialization. To identify the areas of expertise of a scientist, we relied on the “subject categories” listed

in the Web of Knowledge database. ISI’s Web of Knowledge automatically assigns each journal to one or

more general areas of science or “subject categories”. For example, within life sciences, there are

separate subject areas for cardiac and cardiovascular systems (124 journals), gastroenterology (79

journals), pediatrics (120 journals), cell biology (189 journals) and genetics (168 journals). The

Herfindahl index takes into account the proportion of articles written by an individual in each subject

category.

Constellation Attributes

The Team’s Research Impact is the average number of citations received by all individuals in the

team over the five-year window prior to and including the collaboration year. The Team’s Size counts the

number of individuals on the team. In our sample, team size (not including the PI) ranged from 1 to 14.


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Team’s Basic Science Focus is measured as the proportion of individuals with a PhD degree. Two

variables capture the rank (role) diversity in the team. The Team Rank Level is measured as the average

rank across all individuals in the team, such that higher levels indicate a higher academic rank at

disclosure year. We identified six academic rank levels: 1- staff, 2-student, 3-postdoc, 4-assistant

professor, 5-associate professor, and 6-full professor. For the ease of interpretation, we recoded this

variable to take values from 0 (lowest) to 1 (highest academic rank). The Team Rank Range captures the

range of the rank levels within a team.

The Team’s Knowledge Diversity is the team-level counterpart of the individual-level measure

described above. Specifically, we examined all publications of individuals in the team to determine how

disperse or concentrated is the team’s expertise across ISI’s subject categories. We measured the Range

of PI-Team Knowledge Similarity as follows. First, we created a knowledge proximity measure between

the PI and each individual in the team, based on the similarity of their scientific expertise. For each PI-

team member dyad we calculated the angular separation between the vector of PI’s and vector of the

individual team member’s frequency of publications in the multidimensional space formed by the subject

categories in which they published (see, e.g. Sampson 2007 for a similar measure based on patent classes

instead of subject categories). Second, we took the range of this measure across all PI-team member

dyads in a collaboration. Higher values indicate a higher heterogeneity of expertise that a PI shares with

the individuals in a team. To calculate the variable Team’s Experience we first formed all dyadic

relationships within a team. We then measured the shared experience within a dyad as the proportion of

co-authored articles in the total number of distinct articles written by the two individuals prior to the

collaboration year. The Team Experience is defined as the highest value taken by the dyad-level

experience scores in a team. Thus, this variable captures the presence in the team of at least two core

individuals with established joint working routines.


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Estimation

Underlying our matching model of team formation is the idea that the value created by a team (a

PI and a constellation of collaborators) is a function of the complementarity or substitutability between

the team leaders and collaborators.

As our theoretical discussion indicates, there are various mechanisms through which teams may

create value—shared intellectual power (quality), established working routines (experience),

specialization and discipline variety (knowledge). Accordingly, we define the following multidimensional

matching equation:

v (PI, T |β) = β0 * PI: Research Impact *Team: Research Impact +

β1 PI: Research Impact * Team: Size +

β2 PI: Research Impact * Team: Basic Research Focus +

β3 PI: Research Impact * Team: Average Academic Rank +

β4 PI: Research Impact * Team: Academic Rank Range +

β5 PI: Research Impact * Range of PI-Team Knowledge Similarity +

β6 PI: Research Impact * Team: Experience +

β7 PI: Knowledge Diversity * Team: Knowledge Diversity +

β8 PI: Knowledge Diversity * Team: Rank Range + ξPIi + ξTi + εPIiTi , (1)

where ξPIi and ξTi are respectively PI and Team fixed effects, and εPIiTi are match-specific errors.

The value of the matching function v (PI, T |β) can be thought of as the expected payoff that an

individual and a team can create by collaborating. The equation (1) above builds on the hypothesis that

matching is driven by the expectation that PIs of a higher research quality (measured by their Research

Impact) create more value when collaborating with teams of higher research impact (β0), larger size (β1),

teams with whose expertise overlaps less with the PI’s expertise (β5), and teams in which at least two

individuals have higher experience of working together (β6). Although we are agnostic about the sign of

the relationship, we control for the interaction between PI’s Research Impact and a team’s basic focus,

average rank, and rank range. We also control for the possibility that knowledge breadth of the individual


17

(PI’s Knowledge Diversity) drives the matching of PIs with more knowledge diverse teams (β7) and teams

with a higher rank range (β8).

To estimate the function v (PI, T |β) we applied a non-parametric method suitable for studying

matching contexts known as the maximum score estimator (Fox, 2010). The estimator relies on the

principle that observed collaborations are the outcome of a matching process in which the final

configuration of all PI-Team pairings in the dataset are such that no PI and no team can be better off by

unilaterally deviating from the current partnership and forming a different partnership. This condition

translates into a series of inequalities which stipulate that the total value created by any two observed

collaborations in the data (say, v (PIi, Ti |β) created by {PIi and Team Ti} and v (PIj, Tj |β) created by

{PIj and Team Tj} is greater than the total value created by counterfactual collaborations obtained by

switching partners (here, { PIi and Team Tj} and {PIj and Team Ti}):

v (PIi, Ti |β) + v (PIj, Tj |β) > v (PIj, Tj |β) + v (PIj, Ti |β) (2)

In writing the inequalities, we needed to define the boundaries within which exchanges could take

place, formally known as the boundaries of the “matching markets”. The assumption guiding our

empirical strategy was that relevant exchanges could have taken place within each scientific field as the

one of the principal investigator and within the same year. By adopting this convention we defined 33

“matching markets” corresponding to the three fields (basic life sciences, clinical life sciences, and

engineering & physical sciences) and 11 years in the sample.

Coefficient estimates are those that maximize the number of satisfied inequalities among those

that can be written in the sample, here 4331. Let h be a market index, which takes values from 1 to 33.

Let Nh be the total number of collaborations in a market h. The estimates �̂� are obtained by numerically

computing the global maxima of the function that counts the number of correctly predicted inequalities

for all combinations of realized and unrealized matches within all 33 markets:

Q(β)=∑ ∑ 1[v (PIi, Ti |β) + v (PIj, Tj |β) > v (PIj, Tj |β) + v (PIj, Ti |β)]1<i.j<Nh33h=1 (3)


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where 1[.] is an indicator with a value of 1 if the inequality in the parenthesis is true and 0

otherwise.

One of the main advantages of using this estimator is its ability to deal with the fact that

collaborations occur neither randomly, nor without constraints in securing a partnership with the most

desirable individual(s). The matching equation (1) incorporates (through the interaction terms) mutual

preferences of PIs and teams when deciding whom to collaborate with for a research project. The

inequality (2), known as the “local maximization condition” (Fox, 2010) accounts for the partnering

constraints that PI and teams have when picking collaborators who may have better alternatives (see

Mindruta, Moeen and Agarwal, 2015 for an in-depth discussion). There is no assumption about the

distribution of match-specific errors, εPIiTi. However, we made the standard assumption that εPIiTi were not

correlated with the vector of PI and Team attributes, and neither with the fixed effects ξPIi and ξTi.

A particularity of our data is that some of the principal investigators participate in more than one

collaboration in a given year (i.e. we observed up to four collaborations per year, but instances of three or

more collaborations are very rare). After checking the information on the team members’ expertise and

the description of the disclosure carefully, we concluded that all these instances are independent

collaborations. From a technical standpoint, our data is an example of one-to-many matching (one PI

matching with multiple teams). This aspect is fully accommodated by the estimation technique. 6

The method requires us to impose a scale normalization. For this purpose, we set up the

coefficient β0, measuring complementarity between the research impact of the PI and the team, to be

equal to one. Although we do not have a statistical test to confirm complementarity, the simple correlation

between these two variables is 0.30 (see Table 1). This evidence is consistent with the argument that

principal investigators with a higher research impact enjoy higher returns from collaborating with teams

6 Specifically, when we wrote the inequality (2) we took into account whenever the same PI was collaborating with two (or more) different teams, we did not allow for “exchanges” between these teams. For example, assume the following {PI; Team} collaborations: {A; 1} and {B; 2, 3}. The inequality (2) in this case involves exchanging Team 1 with Team 2 in one inequality (while Team 3 remains paired with the principal investigator labeled as “B”) and exchanging Team 1 and Team 3 in another inequality (while Team 2 remains paired with “B”). In addition, because the multiple collaborations in our dataset involved independent projects, we did not model potential complementarities among teams collaborating with the same PI.


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of higher research impact and vice-versa. The magnitude of all other coefficients was assessed relative to

this baseline coefficient. The sign of the estimates matters for distinguishing complementarity from

substitutability: a positive coefficient indicates complementarity and a negative coefficient indicates

substitutability.

Results

Descriptive statistics are shown in Table 1. Table 2 describes the results produced by the

maximum score estimator. We reported both the coefficient estimates and the 95% confidence intervals.

[Table 1 about here]

[Table 2 about here]

The results show that team leaders with higher research quality enjoy stronger complementarities

by collaborating with teams having a basic research focus (β2), teams of lower academic rank (β3), teams

with a higher dispersion in the degree of expertise shared with the leader (β6), and teams with better

working routines in the sense that at least two individuals in the team have a high level of co-authorship

experience (β7). None of the other relationships are significant based on the conventional 95% confidence

interval. The strongest drivers of matching are the heterogeneity of expertise that a PI shares with the

individuals in a team in combination with an experienced team that likely has developed the necessary

routines to manage this diversity.

Post-Hoc Analysis of Value-added

In the final step of our analysis, we used the coefficient estimates and the value creation function

described by the equation (1) to generate counterfactual scenarios in which we formed new pairings for

the “star” principal investigators and their teams (“ star constellations”). We proceeded as follows.

First, we removed each of the 40 stars from the sample one at a time. After each removal, we re-

matched the constellations and the remaining principal investigators, including the other stars, in an


20

optimal way. We then calculated the total value created in the sample (the sum of v for all pairings) with

and without the star scientist in the sample. This difference in the total value in the sample with and

without a star represents the upper bound of the star’s contribution in the market (i.e. see the definition of

the “marginal product” in MacDonald and Ryall, 2004: 1326).

MaxPIj= (� ∑ v (PIj, Tj |β)ℎ𝑗=1

33

ℎ=1)- (� � 𝒗 (PI_i𝐣,𝐓𝐣 |𝛃)

ℎ𝑗=1 )

33

ℎ=1 (4)

where PI-ij indicates that we are indexing across all principal investigators j, less the star “i” where i takes

values from 1 to 40.

Second, we repeated the procedure for all teams matched with our stars in the real data (the “star

constellations”). We removed each of the “star constellations” from the sample one at a time and we re-

matched all principal investigators with the remaining constellations. Like before, we recalculated the

total value of all pairings in the market with and without a star constellation. The difference represents the

upper bound of the constellation’s contribution.

MaxTj= (� ∑ v (PIj, Tj |β)ℎ𝑗=1

33

ℎ=1)- (� � 𝒗 (PI𝐣,𝐓_i𝐣 |𝛃)

ℎ𝑗=1 )

33

ℎ=1 (5)

where T-ij indicates that we are indexing across all teams j, less the “star constellation” corresponding to

the star “i”.

The reassignment procedure of the remaining participants in the market after each removal was

done under two assumptions: a) the general underlying preferences that were driving the matching of

leaders and teams expressed in the equation (1) remained unchanged after the removal of a star, or

respectively, constellation in the market, and b) the optimal assignment of principal investigators and

teams is the one that maximizes the total value created (i.e. the sum of v for all pairings) in a matching

market.

The assumption (b), which lies behind our approach to generating the optimal assignment,

follows closely the solution of the linear programming problem in cooperative game theory discussed by

Shapley and Shubik (1971). The intuition behind it is that the equilibrium solution of a matching game


21

requires the maximization of the aggregate value created by all pairs in the market and not by one

individual agent or an individual pair in isolation. 7

Notice that the first two steps allowed us to calculate the upper bound of a star’s and a

constellation’s contribution. The lower bound of the star’s contribution is the difference between v and the

maximum contribution of the constellation with whom the star collaborates in the real data.

MinPIj= v (PIj, Tj |β)- MaxTj (6)

Likewise, the lower bound of the constellation’s contribution is the difference between v and the

maximum contribution of the star with whom the constellation collaborates in the real data.

MinTj= v (PIj, Tj |β)- MaxPIj (7)

The results of this analysis are presented in Table 3. We summarized the distribution of the

minimum and the maximum contribution. On average, star scientists have higher contribution than star-

teams, both for the upper and the lower bound. This result is consistent with the star spillover effect

discussed in the literature. However, a closer look at the contribution intervals indicates substantive

overlap. Only nine out of forty stars are “net contributors”, meaning that the star’s lower bound is higher

than the corresponding team’s higher bound. Six scientists are in the engineering and physical sciences

domain. As an illustration, Figure 1 plots the contribution intervals for all stars and star-teams in this

domain. In all collaborations involving the other 31 scientists identified as ‘stars’, the team’s higher

bound surpasses the star’s lower bound. In all these cases one cannot unambiguously assert a net

contributor position and conclude that “success is in the stars”.8

7 An alternative would have been to create random matches. However, a random pairing does not satisfies the constraints of partnership formation in a matching context because it does not take into account the constraints of collaborating with the most desirable partner when this partner has better partnering alternatives and capacity constraints (i.e. it can only engage in a limited number of partnerships at a time either because of resource allocation constraints or reputational concerns). In short, the approach of generating the optimal assignment of constellations and principal investigators as a linear programming problem is more theory-grounded than using random pairing as a benchmark. 8 Under the typical economics assumption, the contribution maps into an agent’s (PI, respectively, team) payoffs from a relationship. Under this interpretation, the overlap implies that under certain bargaining scenarios, the teams will appropriate a larger fraction of the surplus as a result of their higher added-value (MacDonald and Ryall, 2004). However, our emphasis here is not on the bargaining and value capture aspects, but rather on the magnitude of an agent’s contribution, as determined by the strategic interactions in the matching market in which collaborations form.


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Conclusion

Much in the literature has been written about the important role of “star” scientists in knowledge

production, innovation and commercialization. This study adds to this literature by investigating the role

of star scientists in the context of their research teams. Our emphasis is on complementarities between

the team leader and other team members, not solely on aggregate team composition or focal individual

contribution. By considering the stars, the constellations, and the interaction between the two, we provide

insight into a key question: what is the relative value contributed by the key players? To what extent is

team performance driven by the great individual or by the great teams?

This paper adds to the literature in two ways. The first substantive contribution is to untangle the

separate impact of stars and their teams, thus informing the ongoing discussion regarding drivers of team

performance. We identified the key matching dimensions of academic stars and their teams that underline

high performance. In addition, we were able to determine the relative value add of each player. The

results are, on average, supportive of a star-to-team spillover effect. However, our analysis raises the

salience of team contribution given that the stars with a ‘net contribution’ are few, while the most

common situation is one where there is no clear dominance of value contribution by the star. This effect

occurs because superior performance appears to be a function of three drivers: 1) individual contribution

of the star, 2) the star’s spillover to the team, and 3) the team’s spillover to the star. This last element is a

product of the resource attraction effect due to the market sorting that allows stars to match with stronger

teams. The fact that we don’t see a clear dominance in the value contribution of the star is an indication

that the team’s spillover to the star is substantial and merits further attention.

Second, we add to the field’s methodological toolbox by developing and employing a new

technique for estimating the contribution interval of an agent in a strategic interaction. This technique

resolves a longstanding challenge by providing a means of empirically estimating the upper and lower

bounds of value parties to a transaction can create and subsequently capture (Gans and Ryall, 2015). The

methodology presented here can easily be extended to other contexts where the strategic interactions


23

depend on the matching between players. Importantly, the structural model employed here deals with the

endogeneity of team formation and team performance and eliminates the need for heroic attempts to

identify relevant natural experiments or viable instrumental variables for dealing with the problem of

mutual selection of transacting parties.


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Variable Mean s.d. 1 2 3 4 5 6 7 8 9

PI Side 1 Research Impact 2194 3647 1 2 Knowledge Diversity .70 .23 0.293 1 Team Side 3 Research Impact 451 885 0.307 0.080 1 4 Size 1.91 1.31 0.179 0.026 0.102 1 5 Basic Research Focus .65 0.4 0.051 0.039 0.065 -0.056 1 6 Average Academic Rank .49 .28 0.016 -0.037 0.458 0.013 0.155 1 7 Academic Rank Range 1.56 .80 0.135 0.054 0.137 0.643 -0.081 0.080 1 8 Knowledge Diversity .53 .29 0.213 0.227 0.309 0.284 0.074 0.520 0.324 1 9 Range of PI-Team Knowledge

Similarity .16 .25 0.212 0.095 0.091 0.513 -0.047 0.008 0.479 0.253 1

10 Experience .53 .47 -0.200 -0.087 -0.086 -0.603 0.039 -0.043 -0.626 -0.293 -0.598


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Table 2. Estimation Results

Coefficient Relationship Point Estimate 95% Confidence Interval

β0 PI: Research Impact * Team: Research Impact 1 Superconsistent β1 PI: Research Impact * Team: Size 0.448 (-0.287, 1.183) β2 PI: Research Impact * Team: Basic Research Focus 2.080 (0.299, 3.861) β3 PI: Research Impact * Team: Average Academic Rank -3.007 (-5.546, -0.468) β4 PI: Research Impact * Team: Academic Rank Range -1.632 (-5.2377, 1.971) β5 PI: Research Impact * Team: Experience 4.223 (1.360, 7.085) β6 PI: Research Impact * Range of PI-Team Knowledge

Similarity 56.489 (26.134, 86.844)

β7 PI: Knowledge Diversity * Team: Knowledge Diversity 0.140 (-4.985, 5.267) β8 PI: Knowledge Diversity * Team: Academic Rank Range 0.028 (-3.155, 3.211) Note The data consist of 33 markets. Coefficient estimates predict correctly 77% of inequalities. Confidence intervals were obtained by re-estimating coefficients in subsamples of 9 markets over 500 runs. The estimation was done in Mathematica, based on a template provided by Santiago and Fox (2014) and adapted for the sample.


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Table 3. Summary results of the Star-PI and Star-Team’s upper and lower bound of contribution. Star’s contribution interval Average s.d. Min Max Maximum (a.k.a. Added-Value) 772.62 824.13 7.29 3116.98 Minimum 11.55 54.050 0 467.49 Team’s contribution interval Maximum (a.k.a. Added-Value) 606.91 725.10 1.00 2646.83 Minimum 0.073 3.64 0 19.48


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Figure 1.

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

Egineering and physical sciences: The estimated intervals of Stars' and Teams' contribution

Stars Teams

stars and their constellations - mack institute for...

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