Diversity in one dimension alongside greater similarity in others: evidence from FP7 cooperative research teams
J Technol Transf
Diversity in one dimension alongside greater similarity in others: evidence from FP7 cooperative research teams
Alex Coad 0
Sara Amoroso 0
Nicola Grassano 0
JEL Classification O 0
0 Joint Research Centre, European Commission , Calle Inca Garcilaso 3, 41092 Seville , Spain
Although diversity between team members may bring benefits of new perspectives, nevertheless, what holds a team together is some degree of similarity. We theorise that diversity in one dimension is traded off against diversity in another. Our analysis of collaborative research teams that received FP7 funding presents robust results that indicators of diversity in several dimensions-diversity of organizational form (universities, firms, etc.), diversity in nationality, and inequality in project funding share-are negatively correlated with each other.
Diversity; Collaborative teams
Boosting open innovation and improving knowledge transfer between research institutions
and third parties such as industry and civil society organizations is one of the key areas of
science and technology policy in Europe. One of the main instruments to foster knowledge
transfer between research institutions and industry across Europe has been the promotion
of research consortia between firms, universities, research centres, and public entities
through the Framework Programmes (FP) for research and technological development.
However, knowledge transfer within a group hinges critically on trusting social relations
between team members
(Alexopoulos and Buckley 2013)
In this regard, an important question relates to how different individuals and actors can be
brought together—despite their heterogeneous backgrounds, cultures and perspectives—to
form an effective research team. When it comes to the issue of cooperative teams, which of the
two opening proverbs is more true? Are project teams composed of members that are different
or similar? The answer, as the reader might have guessed, is probably both. Researchers have
reconciled these two opening proverbs by showing that there are curvilinear effects of
diversity on team formation and performance—that too much diversity (in terms of one
particular variable) might become a liability above a certain threshold, giving rise to an
inverted-U-shaped effect of team diversity on performance
(e.g. Huang and Chen 2010; Von
Raesfeld et al. 2012; Oerlemans et al. 2013)
. Hence, diversity of teams is characterized by a
‘too much of a good thing’ effect
(Pierce and Aguinis 2013)
. However, we depart from the
unidimensional ‘too much of a good thing’ effect and take a different approach to reconciling
the two opening proverbs. We shed new light on the phenomenon by showing that diversity in
one dimension is negatively related to diversity in other dimensions.1 We therefore contribute
to the literature on diversity which, previously, has focused on individual dimensions of
diversity one at a time, without considering how they might interact.
More specifically, focusing on a rich data source on FP7 European collaborative research
projects, we present new insights on how a high level of diversity in terms of type of
cooperating organizations (firms, universities, research organizations, etc.) is associated with
less diversity in other dimensions such as the degree of internationalization and the diversity
of cost share among the members of a research consortium. Our theoretical predictions
receive robust support. Pairwise correlations show that a higher level of diversity for one of
these indicators is associated with a lower level of diversity for the other indicators.
The remainder of this paper is outlined as follows. Section 2 contains the theoretical
background and derives our hypotheses. Section 3 presents our database and contains some
summary statistics on our diversity indicators. Section 4 contains the empirical analysis.
Section 5 concludes.
2 Theoretical background
Diversity is a multidimensional and multicultural construct. Teams can be diverse in many
ways: different educational backgrounds, different genders, different ethnicities, different
ages, different nationalities, different levels of industry experience, etc. Previous research
looked at how team performance depended on team diversity in specific dimensions. The
early literature sought to answer whether diversity is good or bad for performance—
whether it be diversity in education or gender or experience etc.
Diversity can have a positive effect on team performance outcomes such as creativity,
innovation and problem-solving quality, if the team members do not all possess duplicates of
the same skill-sets, but complement each other
(Cox and Blake 1991)
. Diversity of perspectives
1 To illustrate in layman’s terms, one might have more patience for the eccentricities of family members
than similar eccentricities from friends or colleagues—simply because family members are otherwise very
similar in a large number of ways. Another example would be that age differences between team members
are easier to accept if the team members have otherwise similar backgrounds.
is associated with a lower emphasis on conforming to the norms of the past, which can improve
the level of creativity
(Cox and Blake 1991)
. In the context of research teams, actors often seek a
niche for their own work and seek to differentiate themselves
(Dahlander and McFarland 2013)
while accessing the skills of others through the formation of a collaborative research team.
Specialization of individuals in the context of a diverse research team can help avoid the costs of
redundant ideas and unnecessary knowledge overlap, while the partners pool their skills
together to enhance their team capabilities at problem-solving.
However, too much diversity can mean that team members lack the common tacit knowledge
that facilitates communication, which makes it challenging to comprehend one another. For
example, too much international diversity can lead to social categorization, which hinders
information use and knowledge transfer
(Dahlin et al. 2005)
. Functional background diversity
among team members can drive task conflict, while race and tenure diversity can lead to
(Pelled et al. 1999)
. Excessive diversity can also lead to reduced interpersonal liking
and lower psychological commitment
(Lau and Murnighan 2005)
. Actors may therefore seek out
similar actors to quickly winnow the field of potential collaborators
(Dahlander and McFarland
. Similarity engenders a sense of connection and belonging, a common ground and mutual
interest, and a greater sense of interpersonal understanding and security
, while ties between nonsimilar individuals may be more time-consuming to
maintain, may lead to more conflicts, and are more likely to be dissolved
(McPherson et al. 2001)
Early investigations applied linear regression models to see if the relationship was
positive or negative
(Bantel and Jackson 1989; Ucbasaran et al. 2003; Chandler et al. 2005;
Chowdhury 2005; Foo et al. 2005; Amason et al. 2006; Vanaelst et al. 2006)
The next generation of investigations found that the relationship was curvilinear – that a
moderate amount of diversity was good, but that above a certain threshold the gains to
diversity were smaller than the costs
(Dahlin et al. 2005; Horwitz and Horwitz 2007;
Ostergaard et al. 2011)
Our key critique of the literature on team diversity is that the different indicators of
diversity are assumed to operate independently of each other. Each dimension is usually
considered in isolation. An exception would be the ‘faultlines’ approach
(e.g. Lau and
, which examines whether sub-groups might break away from their larger
groups if they have different attributes in more than one dimension—such as a team
composed of young Hispanic women and old Caucasian men. Our approach is different,
because we investigate how diversity in one dimension is compensated for by lower levels
of diversity in other dimensions. We therefore contribute to the literature by taking a new
approach towards conceptualizing diversity, as well as presenting supporting evidence. We
argue that diversity among partners in a research team is costly, in terms of cognitive effort
and team management, and that a research team will compensate for high levels of
diversity in one domain by reverting to lower levels of diversity on other domains.
Our paper can help to explain why literature reviews and meta-analyses suggest that the
previous investigations into diversity in entrepreneurial teams and management teams have
provided mixed and inconclusive results
(Webber and Donahue 2001; Harrison and Klein 2007;
Horwitz and Horwitz 2007)
– i.e. because their models of diversity neglected the
interdependence of these dimensions of diversity, and were thus mis-specified. Including several different
indicators of diversity in a linear regression model (where the dependent variable is team
performance) will not reveal the relationships that exist between the diversity indicators
themselves. Instead, we go beyond the usual finding that the performance effects of diversity are
likely to be curvilinear within individual dimensions of diversity (as emphasized by the existing
literature surveyed above), and instead show that the different dimensions of diversity should
not be taken as independent but that they are inter-related.
Our approach has implications for how team diversity should be conceptualized. In the
context of research teams, for example, our approach would predict that interdisciplinary
research teams will have less diversity in terms of country and organizational form of its
participants than a single-discipline research team, because the diversity in disciplinary
focus is compensated for by reduced diversity in the other domains. Similarly,
collaborative research projects that include universities, firms, and other organizational forms
would probably be more likely to be located in the same country or region, to better
manage the difficulties in communication that arise when agents are heterogeneous (in
terms of organizational form). Our approach would also predict that, for more academic
projects that include only universities (as organizational forms), these projects are more
likely to be international.
We therefore posit a broad hypothesis:
Hypothesis 1: Diversity in one dimension is observed alongside greater similarity in
More specifically, Hypothesis 1 is tested in the context of our dataset, where team
diversity is measured in terms of organizational form (university, firm, etc.), number of
countries, and members’ share of the total project’s cost. These are among the most salient
dimensions of member diversity regarding the formation of collaborative research teams in
the FP7 scheme. For example, diversity in terms of organizational form might complicate
the communication between partners who potentially have different goals and objectives
(e.g. if firms seek the profitable commercialization of innovations while universities pursue
scientific excellence). Nationality is a core part of an individual’s identity
(Dahlin et al.
. Diversity in terms of the nationalities of the members exposes the research team to
different norms and beliefs, possible difficulties in communicating across cultural
(Dahlin et al. 2005)
, as well as higher costs of coordination and management.
Diversity in terms of members’ cost shares might be problematic if the team members have
different roles and varying degrees of status, importance and centrality in the research
We hypothesize that these different dimensions of diversity will be interdependent. A
high level of diversity in terms of organizational form might therefore need to be
compensated for in terms of less diversity in terms of participant countries (to ensure that
participants have a shared tacit knowledge base and cultural background to facilitate
communication), as well as less diversity in terms of member’s share of total project cost
(i.e. where less diversity in terms of project shares means that funding is relatively evenly
distributed, with participants having roles in the project that are more equally matched).
We therefore investigate the following sub-hypotheses:
Diversity in terms of organizational form is negatively correlated with
diversity in terms of participant countries
Diversity in terms of organizational form is negatively correlated with
diversity in terms of members’ share of the total project’s cost
Diversity in terms of participant countries is negatively correlated with
diversity in terms of members’ share of the total project’s cost
3.1 Database description
While most research into team diversity and performance has focused on top management
(e.g. Bantel and Jackson 1989)
or entrepreneurial new venture teams
et al. 2012; Kaiser and Mu¨ ller 2015)
, we focus on collaborative research teams.
Collaboration is increasingly important for scientific research (Jones et al. 2008) as well as for
innovation and technological development
(Hoekman et al. 2013)
, and there is lots of
policy interest in collaborative research projects. However, to date, not much research has
focused on the diversity of members of collaborative research teams.
Our data covers the universe of successful applications to the European Union’s
Seventh Framework Programme for Research and Technological Development (FP7),
which was set up to provide funding for research and technological development in the
European Research Area. The Framework Programmes for Research and Technological
Development are medium-term planning instruments for research and innovation created
by the European Commission The first Framework Programme had a budget of about €3bn,
and began in 1984 and lasted until 1987. The following Framework Programmes had
increasingly large budgets, with €15bn for FP5, €18bn for FP6, and over €50bn for FP7
Rodriguez et al. 2013
). FP7 has now been replaced by ‘‘Horizon 2020’’ (previously named
FP8), which is set to run from 2014 to 2020.
FP7 was the EU’s main policy instrument for funding European research over the period
2007–2013. The majority of FP7 funds were allocated to the block of activities labelled
‘‘Cooperation,’’ that is dedicated for the purposes of the funding of collaborative research
projects. This programme was subsequently divided into 10 thematic areas, the largest of
which were Information & Communication Technologies (€9.11 bn), Health (€6.05 bn),
and Transport (including aeronautics; €4.18bn) (European Commission 2006).
Each project has a single coordinator. Coordinators are the legal entities that are in
charge of the contracts both in legal terms and in scientific terms, since they are ‘legally’
responsible in the eyes of the European Commission for the successful management of the
(Maggioni et al. 2007)
Our data focuses on collaborative research teams that successfully applied for FP7 funding.
Teams that did not get FP7 funding are not included in our dataset. To the extent that our
hypotheses require an indicator of team performance, our indicator of performance would be
that all the teams in our database were successful in obtaining FP7 funding, because only a small
share of applications will succeed. However, considering that FP7 funding was awarded on
many criteria, receipt of FP7 funding (and hence inclusion in our dataset) is a potentially opaque
indicator of performance (although it can also be argued that survival and success in business
environments is an opaque and multifaceted criterion). Nevertheless, given our focus on the role
of diversity in the structure of collaborative teams, we argue that the performance outcomes are
of secondary importance for our present purposes. Indeed, focusing on the composition of
teams and investigating the frequency of teams that are formed, without necessarily linking this
to team performance is, in itself, a worthwhile avenue for research
(Ruef et al. 2003)
An advantage of our dataset is the comprehensive coverage of successful FP7
applications, which means that we have a large number of observations. Our raw data contains
cooperative teams of vastly different sizes—from one or two members to over a hundred
(see Appendix 1). However, having a large number of observations in our dataset will
allow us to crucially narrow down our scope to focus on teams composed of the same
number of members. In our analysis, we focus exclusively on teams with 7–9 participants,
for several reasons. First, Appendix 1 shows that being in a team of size 8 is the most
frequently-observed outcome for team members (if we ignore ‘teams’ of one or two
members), closely followed by teams of size 7 and 9. Our relatively large sample compares
favourably to previous investigations of entrepreneurial teams that have samples of 200 or
(see the review in Coad and Timmermans 2014 Table 1)
. Focusing on teams of 7, 8
or 9 individuals will also be relevant for other research contexts, such as top management
teams that might be composed of a similar number of individuals. Second, a practical
reason is that, unlike smaller team sizes of e.g. 2 or 3 participants, a team size of 7–9
members is sufficient to allow for indicators of diversity (such as integer counts or
Herfindahl indices) to cover an interesting range of possible values. Third, restricting all of
our observations to teams with a similar number of participants (between 7 and 9 members)
will mean that our observations are closely comparable. Indeed, diversity indicators are not
invariant to the number of team members
(Coad and Timmermans 2014)
, and we wish to
avoid any spurious results that might emerge from comparing diversity indicators from
teams of very different sizes.
Our data consists of collaborative research teams that obtained FP7 research funding in
the time window 2007–2013. Each projects then lasted up to 6 years. Although we cannot
rule out that the same collaborative team participated in two subsequent projects during
this time window, nevertheless for simplicity we treat our dataset as a cross-section.
3.2 Indicators of diversity
3.2.1 Diversity of organizational form
FP7 participants can have a variety of different organizational forms: university, private firm,
public body, or Public Research Organization. Table 1 shows that the two most common types
of organizational form are universities (34.2% of cases) and firms (37.5% of cases). As our
indicator of the diversity of organizational forms, we simply take an integer count of the number
of distinct organizational forms
. This simple indicator is easy to understand, and
it is an informative indicator of diversity for our purposes because our observations relate to
teams of the same size. The minimum value is 1 (if all team members are of the same
organizational form, e.g. all are universities or all are private firms) up to a possible maximum of 5.
3.2.2 International diversity
This indicator relates to the number of different countries represented by the project
members. (Countries refer to the organizations involved rather than the nationalities of the
individuals involved.) The number of countries represented is potentially large: although
there are restrictions on the nationality of the FP7 project coordinators (which should be
European nationals, although there are some exceptions), nevertheless non-coordinating
members can come from any country in the world. Again, this is a simple integer count
variable of the number of distinct countries. The minimum value is 1 and the maximum
possible value is 9.
3.2.3 Project cost diversity
Collaborative teams vary in terms of their share of the total project cost. As a further
indicator of team member heterogeneity, we analyse the information on the share of the
total project cost that is distributed to each project member. The project cost share is a
continuous variable that ranges from 0 (for a very large number of members with an
atomistic share each) to 1 (where one member basically accounts for all of the project
cost). Instead of taking an integer count variable (as before), we take the Herfindahl index
which is a meaningful indicator of diversity that has been used in previous research
Foo et al. 2005; Beckman et al. 2007)
. We calculate the Herfindahl–Hirschman index
(HHI) as follows
(Beckman et al. 2007, p157)
where the number of project members n = 7, 8, or 9 and Pi is the project cost share. Our
indicator of project cost diversity based on the HHI index is similar to the two previous
diversity indicators described above, in that low scores correspond to cases of low diversity
(i.e. where all members get the same cost share) whereas large scores correspond to cases
of high diversity (i.e. where there is much inequality in the project cost shares across
members, with one member getting a large share).
Table 2 provides summary statistics for our diversity indicators. The first two indicators
are discrete while the third is continuous. Collaborative research teams vary considerably
according to these three indicators.
We also tried to investigate diversity according to disciplinary theme, to investigate the
structure of interdisciplinary teams. However, in our dataset, all members of a project are
listed under the same project-specific theme, that is the same for all project members.
Therefore we could not investigate heterogeneity in research themes. Relatedly, one might
wish to investigate diversity according to industry affiliations, but upon closer reflection,
the problem here would be that industry affiliations are only allocated to firms and not to
other organizational forms (such as universities or public research organizations).
3.3 Control variables
In our analysis of the diversity of collaborative teams, we want to be careful about pooling
together teams of different sizes. This way, each collaborative team in our sample has a
similar range of possible values for the diversity indicators, and are also closely
comparable because of their similar size. More specifically, firms in our sample consist of either
7, 8 or 9 partners. We therefore include dummy variables for the purposes of controlling
for heterogeneity in group size
(e.g. Pelled et al. 1999)
For collaborative projects of teams with N = 7, N = 8 or N = 9. 3072 observations for 3072 teams
In subsequent regressions, we include some control variables that are observed at the
project-level (and not at the team-member level). First, we control for the potential role of
the total project cost on team diversity, because projects with larger budgets might be more
supportive environments for diversity between team members (e.g. well-funded projects
might allow diverse team-members to come to an agreement more readily than if they are
under financial pressure). Total project cost has a mean of 3,134,319 EUR and a standard
deviation of 2,113,901 for our sample of teams. Second, we control for the project
duration, because projects with longer duration might be more amenable for higher levels of
diversity (if team members are not pressurized by time constraints to reach agreements
with their diverse collaborators). Total project duration has a mean of 35.99 months and a
standard deviation of 9.97 in our sample of teams. Appendix 3 describes the main variables
used in the analysis.
We begin with a correlation analysis, where we compare indicators of diversity in a
pairwise manner, without seeking to explain any one particular indicator (by taking it as
dependent variable). We follow with multivariate regressions, that can include all three
indicators of diversity in the same analytical model, as well as some control variables.
4.1 Correlation analysis
Lower triangular cells: Pearson correlation coefficients (in bold), and associated p values
Upper triangular cells (and in italics): Spearman’s rank correlation coefficients (in bold), and associated
p values. 3072 observations in all cases, corresponding to one observation per team
Taken together, we observe negative pairwise correlations between diversity of
organizational forms and project cost shares, and between diversity of number of countries and
project cost shares (and a non-significant correlation between diversity of organizational
forms and diversity of participating countries). At this stage, we find support for
Hypotheses 1b and 1c, but doubts about Hypothesis 1a.
Robustness of the results was verified by removing the 1% observations at both
extremes of HHI project cost share (which is the only approximately continuous variable
among the three). The correlations are negative and significant, except (as before) in the
case of diversity of organizational forms and diversity of countries.
4.2 Multivariate regressions
We pursue our investigation by applying multivariate regressions, where we can include all
three indicators of diversity in the same analytical model, as well as controlling for the
potentially confounding influence of other variables.
The regression results reported here take the diversity of organizational forms as the
dependent variable (rather than any of the two other diversity indicators), because the
skewness and kurtosis statistics show that it is the diversity indicator that is closest to the
Gaussianity requirement for least-squares estimation. (In further robustness analysis,
however, we verify that our main results hold when taking the two other diversity indicators as the
dependent variable.) Regressions are estimated using Ordinary Least Squares, and for extra
precision in our inference, standard errors are obtained after 500 bootstrap replications.
Table 4 shows the regression results. Each of the three diversity indicators are
significantly negatively related to each other, across two different regression specifications (that
vary according to the inclusion of control variables). Table 4 therefore offers support to
Hypotheses 1a, 1b and 1c.
In both regression specifications, we observed a significant negative correlation between
diversity in one domain, and diversity in another. This finding is reminiscent of the negative
correlation between international diversity and institutional diversity that is visible in in the
Pandza et al. (2011
, Fig. 4) on FP6 data on nanotechnology, although the authors do
not comment in depth on this negative correlation, or provide any theoretical interpretation.
We began by theorising about the benefits and drawbacks of diversity. If a team is diverse
in one domain, it might seek to compensate by having greater similarity in other domains.
For example, interdisciplinary teams might seek similarity in other dimensions such as age,
cultural factors, or geographical base. Our paper therefore carries implications for the
choice of innovation partner—if the partner is very different in certain aspects, then it may
be prudent to seek similarity in others.
Previous research found curvilinear effects—positive effects of diversity on performance for
low values of diversity, but negative effects for high values of diversity. Hence, there exists an
optimum amount of diversity, found at the inflexion point on the curve of performance across
the range of diversity. This can be generalized as the ‘too much of a good thing’ effect
and Aguinis 2013)
. In contrast to the previous literature on the optimal amount of diversity in
any single diversity dimension, our results suggest that this optimum amount of diversity
depends on the amount of diversity in other dimensions. More specifically, we suggest that the
optimum amount of diversity on one dimension is negatively related to the level of diversity in
another dimension. In the context of FP7 collaborative research teams, we observe negative
relationships between the level of diversity in terms of members’ organizational form, the
diversity of country backgrounds, and the diversity of members’ share of the project cost (an
indicator of their status and centrality in the research project). Our analysis shows that all of our
hypotheses are supported, and our results are robust across regression specifications.
Our empirical investigation is not without limits. First, our dataset does not include team-level
performance outcomes (beyond the observation that our data focuses exclusively on successful
FP7 applications). Second, a possible limitation of our dataset concerns whether ‘political’
considerations (e.g. whether evaluators look favourably on projects that include members from
less developed European countries) might influence team formation and funding chances,
beyond purely ‘meritocratic’ concerns. Third, our analysis is undertaken on data that is
essentially cross-sectional in structure—it would be interesting to investigate the dynamics of team
composition and diversity (e.g. some individual characteristics cannot be changed (e.g. gender,
ethnicity) while others change automatically (age) and still others can be manipulated by the
individual (e.g. experience, preferences or team roles)). It is therefore worth investigating the
effect of time on team diversity and team performance
(Steffens et al. 2012; Kaiser and Mu¨ller
. Do teams become more diverse over time? Do individuals make efforts to compensate,
and seek to ‘specialize’ and complement each other in certain dimensions, thus altering the
teamlevel diversity over time? Future work could investigate these issues in more detail.
Future work could use data from other contexts, such as the startup of commercial new
ventures. With regards to FP7 funding, future work might fruitfully compare recipients of
FP7 funding with research teams that applied but were not successful in obtaining funding,
to see if the characteristics of successful teams are different from those of unsuccessful
applicants. Future work might also investigate the effects of diversity on more
conventional indicators of team performance, when ‘excessive’ or ‘disproportionate’ diversity in
one dimension can be traded off against increased similarity in other dimensions, to boost
overall performance. Finally, future research might also focus on the interdisciplinary
nature of cooperative teams (because our data did not allow us to investigate the role of
diverse disciplinary backgrounds within research projects).
To conclude, we suggest a new approach for thinking about the costs and benefits of diversity
for team structure and performance. While previous papers that highlighted the curvilinear
relationship between diversity and performance (with benefits eventually leading to higher costs
as diversity increases), our results suggest that high levels of diversity in one dimension need not
necessarily be a liability if they can be offset by sufficient similarity in other dimensions. An
implication for research teams that are in the process of choosing partners, and for the design of
work groups more generally, could be that they should first choose which dimensions of diversity
are more important for them, in anticipation of the possible compensation of a high level of
diversity in one domain by a lower level of diversity in other domains. For example, a research
team that prioritizes diversity in terms of organizational forms of the team members should be
aware that the research team might have a lower expected level of diversity in terms of country
representation. In the context of pan-European FP7 collaborative research projects, one possible
strategy for enhancing the inclusion of lagging regions in international collaborative teams
Hoekman et al. 2013)
is to ensure that they are otherwise similar to their team partners (in terms
of project share, organizational form, and possibly also in other dimensions).
Acknowledgements We are grateful to Andries Brandsma, Mathieu Doussineau, Koen Jonkers, Bettina
Mueller, Bram Timmermans, and also to the editor Al Link and two anonymous reviewers for many helpful
comments on an earlier draft. Any remaining errors are ours alone. The views expressed are purely those of the
authors and may not in any circumstances be regarded as stating an official position of the European Commission.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
See Fig. 1.
Diversity in one dimension alongside greater similarity in…
See Fig. 2.
b Fig. 2 Pairwise scatterplots of the relationships between diversity indicators, with a linear fit. Scatterplots
with the negatively-sloped linear fit for the correlations between number of organizational forms and number of
countries (top); number of organizational forms and project cost HHI index (centre); and number of countries
and project cost HHI index (bottom). Scatterplots drawn using Stata 14’s ‘‘jitter’’ option, which adds spherical
random noise to the data before plotting to avoid having datapoints plotted on top of each other
Appendix 3: FP7 database
The database includes variables at different levels of aggregation—such as the project level
(e.g. identity of the coordinator) or the participant level (identity of the individual
participant). While some variables are present in the initial dataset, other variables were
constructed from the initial data to gain further insights into the composition of cooperative
networks. Table 5 below contains a description of the variables used in the analysis.
Total cost claimed by a member (coordinator or participant) for their ‘slice
of the pie’, i.e. their share of the project’s activity. A natural logarithm is
sometimes taken for this variable
Total contribution made by the EC to a particular project member’s cost. A
natural logarithm is sometimes taken for this variable
Total cost of the project, before it is divided between project members. A
natural logarithm is sometimes taken for this variable
EC contribution to the total
Total contribution made by the EC to a particular project’s total cost. A
natural logarithm is sometimes taken for this variable
Share of cost
Number of participants
Cost of member’s activity, divided by the total project cost
Number of distinct participating entities (not individuals) on the project
Project duration, in months
Country where the participating entity is located
Organization type, which is one of the following: Higher or secondary
education est. (sometimes relabelled as ‘university’); Private commercial
(sometimes relabelled as ‘firm’); Public body excluding research and
education (sometimes relabelled as ‘public body’); and Research
organizations; as well as some residual categories (‘‘Not defined’’,
‘‘Other’’, and ‘‘Private commercial SME’’ (1 project only))
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