This document discusses research on the relationship between team diversity and innovative capacity. It finds that firms with more diverse research teams in terms of gender, nationality, and field of education tend to have higher innovative capacity, as measured by R&D intensity and innovative efficiency. Specifically, firms with more female researchers showed significantly higher future innovativeness and innovative efficiency. Firms with more foreign and non-STEM researchers also had higher innovativeness but not higher efficiency, possibly because they conduct more basic research. The study uses data from a German R&D survey of over 1,800 firms to analyze these relationships while controlling for firm size, industry, and other factors.

1. THE DIFFERENCE MAKES A DIFFERENCE:
TEAM DIVERSITY AND INNOVATIVE CAPACITY
Julia Schneider, Verena Eckl
OECD Blue Sky III, Ghent, 21 September 2016: Developing novel approaches to measure human capital and innovation

2. Ghent, 21 September 2016 THE DIFFERENCE MAKES A DIFFERENCE: TEAM DIVERSITY AND INNOVATIVE CAPACITY 2
MOTIVATION

3. DOES DIVERSITY HELP TO INVENT BREAKTHROUGH INNOVATIONS?
Ghent, 21 September 2016 THE DIFFERENCE MAKES A DIFFERENCE: TEAM DIVERSITY AND INNOVATIVE CAPACITY 3
» Invention as source of innovation: uncertain process of recombining
components
» Infinite set of potential combinations  individual inventors have only an
infinitesimal understanding of all potential combinations
» Technological breakthroughs need new combinations of components well-
known to the participating inventors
» Diverse groups of inventors with deep experience in different fields
• know more potentially successful combinations of components 
higher probability for technological breakthroughs
• know more about diverse markets, needs and tastes  higher
probability for target group-specific or international innovations
Schumpeter 1939,
Fleming 2001, Ahuja
2000, Parotta et al.
2011, Watson et al.
1993, Drach-Zahavi
and Somech 2001,
Hong and Page 2001,
Osborne 2000,
Berliant and Fujita
2008, Nelson and
Winter 1982

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RESEARCH QUESTION

5. RESEARCH QUESTION: DO THE GAINS OF DIVERSITY OFFSET THE COSTS?
Ghent, 21 September 2016 THE DIFFERENCE MAKES A DIFFERENCE: TEAM DIVERSITY AND INNOVATIVE CAPACITY 5
» Diversity can lead to frictions :
• Mixed teams might have greater problems to effectively communicate
and cooperate
• Levels of trust may also be lower, due to real and perceived differences
between team members
» Costs can be theoretically so high that they offset the gains from diverse
inventor groups
» Can we find empirical evidence that firms that employ researcher
teams with a higher degree of diversity – firms with more foreign,
female, non-STEM researchers – have a better innovative capacity?
Alesina and Ferrara
2005, Becker 1957,
Williams and O'Reilly
1998, Basset-Jones
2005, Zajac et al.
1991, Lazear 1999

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PREVIOUS FINDINGS

7. DO THE GAINS OF DIVERSITY OFFSET THE COSTS? PREVIOUS FINDINGS
Ghent, 21 September 2016 THE DIFFERENCE MAKES A DIFFERENCE: TEAM DIVERSITY AND INNOVATIVE CAPACITY 7
» Innovation-driven firms become more and more diverse in the US:
• successful Start-ups more than twice the average percentage of female
employees than failed Start-ups
• stock market value of innovation-driven firms increases with the
number of women in top management
» But Germany’s firms employ mostly German male engineers
» State of research on impact of ethnical and gender diversity: „Overall, […]
the benefits of diversity are more likely to outweigh the costs in high-
tech/knowledge intensive sectors than in traditional industries,
particularly if the former (latter) are characterized by complex (routine)
tasks, negative (positive) complementarities and innovative (functional)
output.” (Garnero et al. 2014)
» No findings on impact of educational diversity on innovative capacity
Schneider and
Stenke 2016, Dow
Jones 2012, Dezsö
and Ross 2012

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DATA AND METHODS

9. SAMPLE
» R&D survey 2013 of the German business and enterprise R&D (BERD) collected by the SV
Wissenschaftsstatistik (since 1954, full survey every odd-numbered year)
» 2013 = extended version with regard to R&D personnel: nationality, education (subject of
study, scientific degree), entry wages, difficulties in recruiting, relevance and success of
recruitment strategies
» Observations: 1.873 (~14% of 13.589 R&D active firms in 2013)
» Response Bias: overrepresentation of the service/ IT sector and younger scientist, no
significant distortion in terms of firm size, R&D spending, percentage of women
Ghent, 21 September 2016 THE DIFFERENCE MAKES A DIFFERENCE: TEAM DIVERSITY AND INNOVATIVE CAPACITY 9

10. MEASURES: DEPENDENT VARIABLES
» Input Side: Future innovativeness ~ R&D intensity = R&D spending in 2013 / revenues in
2013
• High correlation of input indicator R&D spending and output indicators, i.e. number of
patents, innovation activity , revenues with new or improved products
 Shortcomings:
(1) Uncertainty about future innovation development
(2) Time lag between R&D spending and innovation output
(3) Quantity of R&D covers not necessarily R&D quality
» Output Side: Innovative efficiency ~ R&D efficiency=R&D spending in 2011 / revenues
with new or improved products in 2013
• Well-functioning R&D teams are able to achieve a higher innovation output with steady
R&D spendings
 Shortcomings: Loss of observations
Ghent, 21 September 2016 THE DIFFERENCE MAKES A DIFFERENCE: TEAM DIVERSITY AND INNOVATIVE CAPACITY 10

11. INDEPENDENT VARIABLES = DIVERSITY MEASURES
» Modified Herfindahl index (HI), which measures not as usual the concentration (with 1 =
completely concentrated), but the diversity (1 – HI-concentration).
» Two dimensions of diversity: the “richness”, which refers to the number of defined
categories within a firm, and the “evenness”, which informs on how equally populated
such categories are
» Range from 0 to ½ for two diversity categories, moving from highly diverse (1/2) to
completely homogeneous researchers (0) within a firm
» Three diversity measures of researchers at the firm level:
• (1) gender (female/male)
• (2) education (subject of study STEM/other)
• (3) nationality (German/other)
» Control Variables : size, sector of industry and age of firm
Ghent, 21 September 2016 THE DIFFERENCE MAKES A DIFFERENCE: TEAM DIVERSITY AND INNOVATIVE CAPACITY 11

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RESULTS

13. SUMMARY STATISTICS OF THE STUDY’S VARIABLES
Ghent, 21 September 2016 THE DIFFERENCE MAKES A DIFFERENCE: TEAM DIVERSITY AND INNOVATIVE CAPACITY 14
Variable Number of
observations
Mean Min Max
Future innovativeness (R&D
Intensity)
1842 0.14 0.00 1
Innovative (R&D) efficiency 1153 -1.97 -7.39 4.14
Share of female researchers
over all firms
1515 0.19 0 1
Share of foreign researchers
over all firms
1787 0.05 0 1
Share of non-STEM researchers
over all firms
1736 0.17 0 1
Gender diversity 1533 0.30 0 0.5
Nationality diversity 1758 0.06 0 0.5
Subject diversity 1707 0.11 0 0.5

14. ROBUST REGRESSION COEFFICIENTS: FUTURE INNOVATIVENESS
Diversity Measures Only Educational
diversity
Only Gender
diversity
Only Nationality
diversity
All diversity
measures
together
Educational diversity 0.045* (0.022) 0.030 (0.025)
Gender diversity 0.090*** (0.025) 0.079** (0.027)
Nationality diversity 0.165*** (0.043) 0.173** (0.046)
Chemical industry -0.011 -0.027* -0.015 -0.016
Pharmaceutical industry 0.071* 0.053 0.068* 0.061
Electronical industry 0.044*** 0.048*** 0.036*** 0.037***
Mechanical Engineering -0.007 0.001 -0.010 -0.009
Automotive industry 0.031 0.045* 0.037* 0.031
ICT sector 0.098*** 0.103*** 0.092*** 0.095***
Knowledge intensive Services 0.177*** 0.170*** 0.174*** 0.184***
< 100 Employees 0.069*** 0.071*** 0.071*** 0.099***
100-249 Employees 0.010 0.010 0.011 0.010
250-499 Employees 0.010 0.013 0.009 0.008
Firm Age -0.002*** -0.001*** -0.001*** -0.002***
Constant 0.069*** 0.048*** 0.061*** -0.003
R-squared 0.219 0.229 0.230 0.246
No. 1600 1724 1645 1346
Ghent, 21 September 2016 THE DIFFERENCE MAKES A DIFFERENCE: TEAM DIVERSITY AND INNOVATIVE CAPACITY 16
» Positive relationship between each diversity measure and the firm’s future
innovativeness, independent from the other predictors in the model
(robust linear regression)

15. ROBUST REGRESSION COEFFICIENTS: INNOVATIVE EFFICIENCY
Diversity Measures Only Educational
diversity
Only Gender
diversity
Only Nationality
diversity
All diversity
measures
together
Educational diversity 0.075 (0.218) -0.067 (0.233)
Gender diversity 0.711*(0.314) 0.846** (0.326)
Nationality diversity -0.121(0.290) -0.374 (0.325)
Chemical industry 0.026 0.011 0.025 0.028
Pharmaceutical industry 1.214*** 1.216*** 1.302*** 1.148***
Electronical industry 0.573*** 0.640*** 0.571*** 0.515***
Mechanical Engineering -0.350** -0.184 -0.319** -0.422***
Automotive industry 0.701** 1.218*** 0.796** 0.753**
ICT sector 0.546*** 0.551** 0.572*** 0.637***
Knowledge intensive Services 0.835*** 0.791*** 0.862*** 0.841***
< 100 Employees 0.983*** 0.928*** 0.894*** 0.898***
100-249 Employees 0.222 0.177 0.154 0.142
250-499 Employees -0.148 -0.192 -0.172 -0.093
Firm Age -0.008** -0.009** -0.009** -0.008*
Constant -2.730*** -2.740*** -2.627*** -2.837***
R-squared 0.228 0.223 0.220 0.243
No. 1053 906 1074 904
Ghent, 21 September 2016 THE DIFFERENCE MAKES A DIFFERENCE: TEAM DIVERSITY AND INNOVATIVE CAPACITY 17
» Positive relationship between gender diversity and the firm’s innovative
efficiency
» No significant relationship between other diversity measures

16. DISCUSSION
Ghent, 21 September 2016 THE DIFFERENCE MAKES A DIFFERENCE: TEAM DIVERSITY AND INNOVATIVE CAPACITY 20
» We find empirical evidence that firms that employ researcher teams with a
higher degree of diversity – firms with more foreign, female, non-STEM
researchers – have a better innovative capacity
• Firms that employ more female researchers have significantly higher future
innovativeness and innovative efficiency
• Firms that employ more non-STEM and more foreign researchers have
significantly higher future innovativeness but not a higher innovative efficiency
 firms conduct more often basic and applied research instead of product
development - which often does not directly lead to new and innovative,
marketable products
» To achieve causality, the quantitative analyses needs the transformation event on
the firm level over time, the before and after conditions, to investigate the impact
of the event “diversity” and a sufficient time lag to develop influence.

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