International Workshop “Economic Impacts of Immigration and Population
Diversity”, University of Waikato, 11-13 April 2012
Labour diversity and firm’s innovation
Evidence from Germany
Annekatrin Niebuhr (IAB/CAU), Cornelius Peters (IAB)
1
Agenda
 Introduction
 Literature – theory and empirics
 Data and regression analysis
 Results
 Conclusions
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Introduction
Motivation
 Significant changes in workforce composition
• Demographic change
• International mobility of labour
• Participation of women
 More than 2 million foreign workers in Germany (7.3% of all
employees), 140,000 high-skilled foreigners
 Expected net annual in-migration between 100,000 and 200,000
 Share of workers aged 50-65 years will increase from currently 31%
to 40% in 2025
Effects of workforce composition on economic performance and in
particular on innovation?
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Introduction
Economic performance of firms and staff diversity
 Firm’s competitiveness depends on resources and capabilities, in
particular on human capital
 Skills and knowledge of workers influence productivity and
innovation (Barney 1991)
 Important role for accessing and absorbing knowledge spillovers
(Cohen & Levinthal 1990)
 Human capital is a critical resource in R&D activity
 Effects of workforce composition and in particular heterogeneity of
workers?
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Theoretical background
Costs and benefits of diversity
 Labor is not homogenous, worker heterogeneity not restricted to
educational attainment and experience
 Benefits: workers of different cultural backgrounds, age and gender
different skills, diverse knowledge, various perspectives / ideas
facilitate problem-solving and stimulate innovation
(Hong & Page 2004; Keely 2003; Alesina and La Ferrara 2005)
 Costs: staff heterogeneity might create communication barriers,
cause misunderstanding and conflict in the workplace
reduce competitiveness (Basset-Jones 2005; Lazear 2000)
No clear-cut implications regarding economic effects
of worker heterogeneity
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Empirical literature
Empirical evidence on effects of staff diversity
 Different strands of literature: heterogeneity at different levels of
aggregation (units of observation), various dimensions of diversity
and outcome variables
 Human resource management literature: Case studies, experimental
settings, work-team compositions (especially top management), e.g.
Kilduff et al. (2000), Bantel & Jackson (1989)
 Aggregate level (industry, region): focus on cultural background,
impact on employment, entrepreneurial activity, innovation, e.g.
Suedekum et al. (2009), Audretsch et al. (2010), Ozgen et al. (2011a)
 Evidence on effects of labour diversity on organizations / firms is
scarce: Brunow & Blien (2011) and Parrotta et al. (2010) consider
productivity effects
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Empirical literature
Empirical evidence on effects of staff diversity
 Little evidence of impact of diversity on innovation at firm level:
Ozgen et al. (2011b), Østergaard et al. (2011), Parotta et al. (2011)
and Söllner (2010)
 Few studies consider heterogeneity with respect to different
dimensions (Herring 2009
gender and ethnicity)
 Econometric problems: unobserved heterogeneity, endogeneity of
diversity
 Evidence on economic effects of worker heterogeneity is ambiguous
so far and findings with respect to innovation at firm level are scarce
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Empirical literature
Contribution of the paper
 Evidence on the impact of worker diversity on firm innovation in
Germany
 Consider impact of several dimension of staff diversity (gender, age,
and cultural background)
 Diversity of entire staff of establishment, diversity of specific groups
(R&D staff)
 Differentiate between radical and incremental innovations
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Data and regression analysis
Data set
 Panel data set: 18,668 observations for 4,416 plants (1996 – 2008)
 Information about: R&D activity; innovations; plant characteristics (age,
size, region, industry); worker characteristics (gender, age, nationality,
education, occupation)
 Sources: IAB Establishment Panel, Establishment History Panel, IAB
employee history
 Only plants at least 5 employees subject to social security notification
and at least 3 observations
 Only private-sector
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Data and regression analysis
R&D and innovation in the IAB Establishment Panel
 Information about innovations in 1998, 2001, 2004, 2007, 2008, 2009
 Innovation during the 2 past years
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Data and regression analysis
Regression model – logit ML
 Binary dependent variable: innovation (Yes / No)
 Diversity measures
gender, age and cultural background
 Controls: plant size, plant age, R&D staff, human capital (qualification structure),
average age of staff, industry dummies, time fixed effects, region type
(agglomerations, urbanised and rural regions), dummy East Germany
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Data and regression analysis
Econometric issues
 Unobserved heterogeneity – random effects model
• No convergence of fixed effects models
 Endogeneity of staff diversity
• Innovation output / performance of firms may impact on recruitment and
composition of workforce
• Parotta et al. (2011): firms might leverage staff heterogeneity to increase
their innovation output
• IV estimation: instruments
basic idea of matching approach: control group
of “twin” establishments from BHP to calculate diversity measures (year,
industry, size, age, region type, East/West)
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Data and regression analysis
Measuring cultural diversity
 Number of cultural clusters: 12 clusters, based on GLOBE clusters
(common language, geography, religion, historical accounts, empirical
studies; Gupta et al., 2002)
 Share of foreign workers
 Share of foreign
number of cultural clusters
 Alternative measures: inverse Herfindahl index, share of foreign
Herfindahl index
inv.
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Data and regression analysis
Measuring age diversity
 Age range to capture positive effects (Dawson 2007): agemax
agemin
 Maximum age gap to capture negative effects (Dawson 2007): largest
difference between two adjacent workers i and j
max (min (agei
i
j
age j )), agej
agei
 Alternative measure: standard deviation (expecting inverse U-shaped
relationship)
Measuring gender diversity
 Share of minority: min(share of female workers; share of male workers)
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Results
Pooled logit for innovation
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Results
Random effects logit for innovation
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Results
Pooled logit for specific types of innovations
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Regression results
Random effects logit for specific types of innovations
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Conclusions
Main results (so far)
 Significant effects of different dimensions of labour diversity on
innovation
 Diverse staff seems to offer wider pool of skills, knowledge and problem-
solving capabilities
 But: evidence on benefits and costs
adverse effects resulting from
communication barriers and possible conflicts
 Endogeneity: correlation
causal effects
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Conclusions
Conclusions
 Important implications for recruitment strategies of firms and for policies
adopted to deal with demographic change (in particular population aging)
and immigration policy
 Take into account beneficial effects of workers heterogeneity, minimizing
adverse effects resulting from communication barriers and possible
conflicts
 Migration policy – improve conditions for skilled immigrants,
entrepreneurs and foreign students – still significant barriers (income
threshold)
 Immigration as an instrument to influence both cultural diversity and
heterogeneity with respect to age
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Back-up
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Cultural clusters
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Random effects Logit, alternative measures
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Pooled Logit, complete results (large sample)
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Random effects Logit, complete results (large sample)
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