migration and innovation

8/17/2019 Migration and Innovation

1/24

Migration and innovation: Does cultural diversity matter for

regional R&D activity?*pirs_271 563..586

Annekatrin Niebuhr1

1 IAB Nord, Regional Research Network, Institute for Employment Research, Projensdorfer Straße 82, D-24106

Kiel, Germany (e-mail: [email protected])

Received: 27 April 2009 / Accepted: 20 September 2009

Abstract. Recent theoretical research deals with economic costs and benefits of cultural diver-

sity related to immigration. However, empirical evidence regarding the impact of cultural

diversity on economic performance is still scarce. We analyse the effect of cultural diversity of

the labour force on patent applications for a cross-section of German regions. The results

suggest that differences in knowledge and capabilities of workers from diverse cultural back-

grounds enhance performance of regional R&D sectors. As regards innovation, the benefits of

diversity seem to outweigh the costs caused, for example, by communication barriers.

JEL classification: C21, J61, O31

Key words: Cultural diversity, regional innovation, knowledge production function, Germany

1 Introduction

The significance of the immigration of qualified workers will rapidly increase in the ageing

European economies since demographic change will cause a decline of the labour force and asharp increase of the average age of workers. Foreign workers are already an important factor

of the German economy. In 2004, almost 7% of all employees in Germany have foreign

nationality. More than 100,000 highly skilled foreigners with a university degree work in

Germany. Zimmermann (2005) notes that, in spite of the rising importance of migration, the

issue is still controversial, and the understanding of the effects of international labour mobility

is rather limited. Research on the economic consequences of migration has mainly focused on

labour market effects and, more precisely, on the question whether immigrants depress wages

and increase unemployment of native workers. Many analyses stress substitution effects among

* I would like to thank Christoph Grenzmann (Stifterverband für die Deutsche Wissenschaft) for the generousprovision of regional R&D data and Andrea Stöckmann for excellent research assistance. Financial support from theVolkswagen Foundation is gratefully acknowledged as part of the Study Group on Migration and Integration ‘Diversity,I i d h E ’ I h k H b B k E kh d B d S f F h R di W l d

doi:10.1111/j.1435-5957.2009.00271.x


2/24

native and foreign workers. However, taking into account that labour is not homogenous, the

impact of immigration depends on whether migrants are skilled or unskilled and on labour

market conditions in the host country.

The objective of this paper is to provide evidence on the impact of migration on innovation,

a subject that has not received much attention in the migration literature up to now (see Hunt and

Gauthier-Loiselle 2008). This paper’s analysis differs from many previous studies that focus onlabour market effects of immigration.1 Moreover, there are only a small number of papers that

investigate the significance of labour mobility as regards knowledge spillovers and innovation

(e.g., Almeida and Kogut 1999; Kim and Marchke 2005; Simonen and McCann 2008). The

findings so far suggest that the mobility of R&D staff is associated with the transfer of

knowledge and positively affects patent applications. However, these studies do not consider the

heterogeneity of researchers with respect to their cultural background. This is another aspect that

differentiates this analysis from other studies. We do not restrict heterogeneity of labour to the

level of education only. Due to their different cultural backgrounds, it is likely that migrants and

native workers have fairly diverse abilities and knowledge. Thus, there might be skill comple-

mentarities between foreign workers and native in addition to those among workers of differentqualification levels. Presumably foreign and native workers of the same educational level are

imperfectly substitutable groups because of cultural differences. Fujita and Weber (2004) argue

that cultural diversity of the labour force might be of special importance for R&D activity since

the generation of new products and ideas heavily relies on individual talents and skills from

diverse educational and cultural environments.

The possibility that diversity can enhance productivity, innovation, and growth has already

been considered in the economic literature. Diversity might refer to economic diversity, i.e.

heterogeneity of firms and industries, or to the diversity of people. Most studies have concen-

trated on the impact of economic diversity and the effects of a diverse urban environment rather

than on cultural or ethnic diversity of people.2

According to Jacobs (1969), diversity of geo-graphically proximate industries promotes innovation and growth in cities. Glaeser et al. (1992)

as well as Feldman and Audretsch (1999) provide corresponding empirical evidence for U.S.

cities. Duranton and Puga (2001) and Henderson et al. (1995) investigate the role that a

diversified urban environment plays in fostering innovation and attracting innovative industries.

Romer (1990) highlights the significance of a variety of intermediate inputs for productivity in

his seminal endogenous growth model.

As regards the diversity of people, Keely (2003) argues that interaction between heteroge-

neous skilled workers gives rise to knowledge spillovers and produces new research ideas. Hunt

and Gauthier-Loiselle (2008) as well as Kerr and Lincoln (2008) investigate the effect of skilled

immigration on innovation for a cross-section of U.S. States. According to the former study,immigrants patent at much higher rates than natives. This patenting advantage is due to the

above average share of degrees in science and engineering among skilled immigrants. Kerr

and Lincoln (2008) show that invention increases with higher admission levels through the

direct contribution of immigrants to patenting. Empirical evidence provided by Anderson et al.

(2005) suggests that creativity is greater in regions marked by more diverse employment bases.

Finally, Audretsch et al. (2009) consider the impact of cultural diversity of the labour force on

entrepreneurship.

While there is an emerging theoretical literature dealing with the economic effects of

cultural diversity (e.g., Lazear 1999b, 2000; Fujita and Weber 2004), there are surprizingly few

empirical studies within the field of economics. Theoretical models consider different costs and

564 A. Niebuhr


3/24

benefits of diversity and specify various linkages between diversity and economic performance.

However, corresponding empirical work, that can help determine whether positive or negative

effects of cultural diversity prevail, remains scarce. Until now, there has been mainly cross-

country evidence and studies focusing on growth and productivity effects in U.S. regions

(Easterly and Levine 1997; Ottaviano and Peri 2005, 2006). Up to now, evidence on the impact

of skilled immigration on innovation, provided, for example, in Chellaraj et al. (2008), Hunt andGauthier-Loiselle (2008), and Kerr and Lincoln (2008) is restricted to the U.S. Most investiga-

tions that analyse the relationship between innovation input and output fail to take cultural

diversity into account (e.g., Anselin et al. 1997; Bottazzi and Peri 2003; Bode 2004,). The aim

of this paper is to investigate the impact of cultural diversity on regional innovation in Germany.

Therefore, we extend the knowledge production framework to analyse whether a more diverse

labour force, from a cultural point of view, fosters innovation due to production complementa-

rities, or whether negative effects of diversity, caused, for example, by language barriers,

outweigh the benefits. Due to data restrictions, we define cultural diversity as diversity of

workers’ nationality rather than ethnicity or cultural background. Regionally differentiated

information on country of origin of inhabitants and employees is not available in German officialstatistics.

The rest of the paper is organized as follows. In Section 2, the theoretical framework of the

analysis is outlined. Production complementarities and costs associated with cultural diversity

are discussed. The data set applied in the empirical analysis is described in Section 3. An

important issue of the investigation concerns the measurement of cultural diversity. In Section

4, we introduce the applied diversity indicators and provide some empirical evidence of cultural

diversity in German regions. We employ the knowledge production function approach to

investigate the impact of cultural diversity on regional innovation capacity. The corresponding

regression model and some robustness issues are discussed in Section 5. The regression results

are presented in Section 6. Conclusions follow.

2 Theoretical framework

Ottaviano and Peri (2006) argue that skills of foreign workers might complement those of the

native labour force. In their model of multicultural production, different cultural groups provide

different services. Diversity has a positive impact on regional productivity. However, heteroge-

neity also hampers the exchange between different cultural groups: there are adverse produc-

tivity effects because of cultural distance. Other authors also recognize that there is a trade-off

with respect to heterogeneity. Lazear (1999a, 2000) considers the positive productivity effects of ethnic diversity, but there are costs of diversity arising from barriers to communication caused

by different languages and cultures.3 Thus, according to the literature, there appears to

be an optimal degree of diversity which is influenced by the nature of production. Some of

the literature on this theme also examines the significance of institutions in this context. An

important result of this research is that the implementation of growth enhancing effects of

diversity may require a specific set of rules, or regulatory framework. Ottaviano and Peri (2006)

emphasize the role of a core of shared norms (integration) that might constitute a prerequisite for

realizing the potential benefits of diversity.

There appears to be a link between the costs and benefits of diversity on the one hand and

the concept of ethnic identity described in Constant et al. (2006) on the other hand. Accordingto the authors, migrants start out from their ethnicity, i.e. permanent characteristics associated

565Migration and innovation


4/24

with the country of origin, and then develop their ethnic identity as they are exposed to the

culture and values of the host country. Ethnic identity is defined as the balance between

commitments with the host country and commitments with the country of origin. Constant et al.

(2006) distinguish four states of ethnic identity: assimilation, integration, marginalization and

separation. Assimilation seems to imply a strong decline of both costs and benefits of cultural

diversity since it is characterized by a strong identification with the host country and conformityto the corresponding norms and codes. With respect to the economic effects of diversity,

integration might be interpreted as the best state because it involves commitment to the host

society, but also a strong dedication to the culture of origin, thus, still ensuring high benefits but

relatively low costs of diversity. In contrast, in case migrants are primarily identified as mar-

ginalized or separated, cultural diversity may mainly entail high costs.

The benefits of diversity might be of particular importance in the R&D sector, whereas in

industries specialized on more standardized forms of production it might be more likely that

costs of a diverse labour force outweigh the positive effects. Alesina and La Ferrara (2005) argue

that cultural diversity may lead to innovation and creativity since it involves variety in abilities

and knowledge. Fujita and Weber (2004) note that knowledge production relies heavily on thetalents and skills of employees coming from a wide range of cultural backgrounds. The nature

of R&D activity calls for interaction between different workers and a pooling of different ideas

and abilities. Berliant and Fujita (2008) also refer to the significance of cultural diversity for

knowledge creation and transfer. The heterogeneity of people is important for the creation of

new ideas.

As outlined by Alesina and La Ferrara (2005), ethnic diversity can affect economic perfor-

mance in different ways. Diversity might have a direct impact on economic outcomes via

different preferences or by influencing individual strategies. Moreover, diversity might have an

influence on the production process. Our analysis focuses on the latter approach. As in Romer

(1990), we assume that the production of new knowledge in region i requires people engaged inR&D. Following Ottaviano and Peri (2005), we augment the production function to allow for

effects of cultural diversity of the workforce. R&D staff Lin differs with respect to nationality n.

In order to develop new designs of products Pi, they can make use of the existing technological

knowledge A i:

P div A Li i i inn

N

= − ( )[ ] ( )−

=∑1 1

1

τ α α α

(1)

where t (divi) ∈ (0,1) is transaction cost that is an increasing function of cultural diversity. Thus,

only a fraction [1 - t (divi)] of inputs is available for production due to communication barriersand other differences between cultural groups which hamper interaction among them. The

additively separable function with respect to labour input implies that, given the total number of

researchers, output increases as jobs are distributed across more groups of workers. With a given

number of nationalities N , it is more productive to distribute employees evenly across the N

groups than to concentrate on a single nationality. As regards the qualification level, we assume

labour to be homogeneous. Heterogeneity only refers to the cultural background of the workers.4

Researchers with the same educational attainment but different nationality are not perfect

substitutes for each other. With this functional form, productivity in R&D is higher, the larger

the number of nationalities and the more balanced their supply. The magnitude of the positive

impact of diversity depends on the elasticity of substitution among nationalities s =

1/(1 - a ).

566 A. Niebuhr


5/24

In order to differentiate the impact of a simple increase of R&D staff from the effect of

diversity, we multiply Equation (1) by ( Li / Li)a

P div A L L L div A L d i i i i in i

n

N

i i i= − ( )[ ] ( ) = − ( )[ ]−

=

−∑1 111

1τ τ α α α α α α α iiv

i (2)

Cultural diversity of R&D workers is given by div L Li in in

N

= ( )=

∑ α 1

. The diversity index is

influenced by the number of nationalities that are present in the regional workforce and by the

distribution of R&D employment across nationalities.

The marginal effect of diversity on knowledge output is given by:

∂

∂ = − ( )[ ] − − ( )[ ] ⋅

∂

∂ ⋅−

−P

div A L div div

divdivi

i

i i i i

i

i1 11 1α α

α α τ α τ

τ

(3)

Thus, the size and direction of the diversity effect is influenced by two factors: the elasticity of

substitution between nationalities, i.e. the strength of the complementarity, and the transaction

costs associated with diversity.

With respect to the trade-off associated with cultural diversity, some issues emphasized by

Lazear (2000) are noteworthy. Lazear argues that the gains from diversity should be greatest if

different groups have information sets that are disjoint because then individuals can learn a great

deal from each other. In contrast, if knowledge and capabilities completely overlap, there are no

benefits from diversity. Moreover, the information and knowledge must be relevant for other

groups. With respect to innovation it is most likely that primarily the know-how of high skilled

foreigners is relevant. The educational attainment of workers might also affect the costs of diversity. Costs that arise due to language barriers might be lower among employees with a

university degree. This is in line with Keely (2003) who argues that interaction cannot take place

if the technology gap between workers is too large. Thus, the skill level of workers probably

influences both the relevance of information and the barriers of information exchange.

3 Data

Point of departure of our empirical analysis is the knowledge production function outlined in

Section 2 that links R&D input to R&D output, namely, new products, processes and ideas.

Thus, we first of all need adequate proxies for regional innovation and R&D input to investigate

the impact of cultural diversity on knowledge production. Regional data on patent applications,

used as a measure for knowledge output,5 and on R&D inputs in Germany are available on the

county level (NUTS 3) and for planning regions (so-called Raumordnungsregionen) which

consist of several NUTS 3 regions linked by intense commuting. We have to restrict the

regression analysis to planning regions due to some data restrictions for NUTS 3 regions.

Overall, our cross section contains 95 regions. Data for these regions is generated by aggregation

of NUTS 3 level information if data is not available for planning regions. Furthermore, the

analysis takes into account the region type. Starting from a classification based on a typology of



6/24

settlement structure according to the criteria population density and size of the regional centre,

we differentiate between agglomerated, urbanized and rural regions.6

Patent applications, applied as an indicator for innovative output of the region, comprise

patents published by the German and the European patent office that have been assigned to the

innovators’ region of residence. As Bode (2004) notes, this approach avoids potential mismea-

surement due to centralized patenting of multi-site companies. Annual patent data is availablefor the period 1995 to 2000.7 Information on R&D input was provided by the German Stifter-

verband. R&D data include R&D staff as well as R&D expenditure of commercial firms. The

data come from a census and are available for 1995, 1997 and 1999. However, we can only use

data for 1997 and 1999 in our analysis. Data for 1995 is not compatible due to some changes in

the delineation of regions. Thus, the investigation is restricted to a panel data set with only two

observations in the time dimension. Finally, we include several explanatory variables in the

regression model based on employment data provided by the German Federal Employment

Agency for the period 1993 to 2000. The employment statistic covers all employment subject to

social security contributions.8 The information is given on the NUTS 3 level and refers to

workplace location. We use employment data differentiated by nationality, educational level,branch, occupation, and firm size in order to generate our diversity measure and several control

variables that enter into the regression model.

4 Regional disparities in cultural diversity of the labour force

We apply different methods to quantify cultural diversity in order to check the robustness of

results with respect to a variation in measurement. A first indicator of cultural diversity is rooted

in the literature on growth effects of ethnic fragmentation (e.g., Easterly and Levine 1997). In

these studies, the probability that two randomly drawn individuals belong to two differentgroups is frequently used as a measure of fragmentation. The corresponding diversity measure

is calculated as 1 minus the Herfindahl index of concentration across groups:

DIV H sit int n

N i

_ = −=

∑1 21

(4)

where s int is the share of employees with nationality n among all employees of region i in year

t . N i is the number of different nationalities actually present in region i. Ottaviano and Peri

(2006) note that this indicator accounts for both richness of the distribution, i.e. number of

nationalities, and a relatively even distribution across nationalities. Thus, according to thismeasure, cultural diversity will increase if the number of nationalities rises or if the shares of

different nationalities in employment converge. A disadvantage of this diversity measure is,

however, that the index assigns disproportionately high weights to the largest nationalities. The

result is largely driven by the share of the dominant population group, i.e. the natives. The

Herfindahl measure will therefore be highly correlated with the employment share of Germans

(foreigners, respectively).

An entropy index, such as the Theil index, offers a more adequate way of measuring cultural

diversity than the Herfindahl index. The corresponding diversity measure is defined as follows:

6 Four planning regions had to be merged due to restricted data availability. The classification has been developed bythe Federal Office for Building and Regional Planning. For details see URL: http://www.bbr.bund.de/raumordnung/

568 A. Niebuhr


7/24

DIV T s sit int int

n

N i

_ ln= − ⋅ ( )=

∑1

(5)

The maximum of the Theil index is ln( N i), indicating that employment is evenly distributed

across the N i different nationalities in region i, i.e. sint = 1/ N i, "n. If the labour force consists of just one ethnic group, the index takes the minimum value ln(1) = 0. The marginal contribution

of an additional worker to cultural diversity is ceteris paribus the higher, the smaller the ethnic

group to which she belongs. The Theil index is a measure of cultural diversity that reflects both

the share and the variety of the foreign population in the region under consideration (Audretsch

et al. 2009).

And finally, we apply the so-called Krugman index to measure cultural diversity of

employment:

DIV K s N it int

in

N i

_ = −=∑ 1

1(6)

The index is calculated as the sum of absolute differences between the employment shares of

nationalities and a reference that is given by 1/ N i. Thus the reference corresponds with an even

distribution across nationalities. This implies that a value of DIV _K it = 0.5 indicates that at least

one quarter (0.5 ¥ DIV _K it ) of the total workforce has to change nationality for the employment

distribution to correspond exactly to the reference distribution (see Bickenbach and Bode 2008).

So in contrast to the diversity measures introduced above, a low Krugman index points to a

relatively diverse workforce.

Whereas most studies on the effects of cultural diversity are based on population data, weuse employment data instead. The advantage of our measure is a closer connection to the

production process. Moreover, nationality defines cultural identity of employees in the present

analysis. Country of birth is the most widely used indicator in this context. However, informa-

tion on country of birth is not available in German statistics. Applying nationality to determine

cultural identity has advantages and drawbacks. Referring to nationality implies that naturalized

citizens do not enter into the diversity measure as ‘foreign’ persons. However, using country of

origin as a definition of the foreign workforce implies that we do not consider people with a

migration background born in Germany, unless we have information on the country of birth of

the parents. Naturalized employees probably tend to be more successful with respect to educa-

tional attainment and labour market integration due to the terms of naturalization in Germany(minimum duration of stay, required language skills). Therefore, our diversity measure might be

imprecise with respect to the highly qualified labour force and affected by a downward bias.

The indicators of cultural diversity in this study are based on regional employment data

differentiated by occupation, educational attainment, and nationality. We can differentiate

between 3 levels of education (no formal vocational qualification, completed apprenticeship,

university degree) and 213 nationalities. We use total employment and R&D employment to

calculate diversity measures. R&D employment is defined on the basis of occupations. We

consider engineers, chemists, physicists, mathematicians, technicians, other specialized techni-

cal staff, and natural scientists. Different diversity indices are calculated for total employment

and R&D employees: aggregate measures as well as qualification-specific indices correspond-ing to the levels of education mentioned above. By considering the cultural diversity of the

labour force at different qualification levels, we can check whether education matters, i.e. taking



8/24

The share of foreign employees in Germany amounts to 7.1% in 2000. 9 This corresponds

with a value of the overall diversity measure (Theil index) of 0.47. Table 1 shows summary

statistics and results of different diversity measures for regions types. Agglomerated regions

achieve highest levels of cultural diversity, whereas rural areas are marked by a relatively low

diversity of the workforce. Moreover, there are distinct differences between East and West

Germany. In East German regions, cultural diversity is significantly lower than in the western

part of the country. The Herfindahl and the Krugman index show similar spatial patterns. In

particular, the regional distribution of the Herfindahl and the Theil index correspond very

closely. The Krugman index differs somewhat as density at the upper tail of the distribution

tends to be higher than for the other measures. Furthermore, density increases more quickly at

the lower tail of the distribution.

There are also distinct differences between the diversity measures for high skilled, R&D,

and total employment. Cultural diversity is higher among all workers than among high skilled

and R&D employees. This might reflect to some extent a downward bias in measurement of

diversity for high skilled workers mentioned above. Due to the terms of naturalization, such as

skill requirements, one might expect that naturalized workers are on average characterized by a

higher level of educational attainment than foreign employees. This might result in a bias of a

diversity measure that is solely based on foreign workers. But recent empirical evidence

suggests that there are no significant differences in the share of the high skilled among foreign-

ers and the naturalized population in Germany (see Statistisches Bundesamt 2009). Moreover,

the overall number of nationalities is likely to be lower in high skilled and R&D employment

than in the total work force. However, for R&D employment, cultural diversity is higher among

high skilled as compared to total R&D staff.

The most diversified regions are Stuttgart, Munich and Frankfurt, highly agglomerated

regions in the South-West of Germany (see Figure 1). There are no cities from the northern part

of the country among the leading regions. Lowest diversity measures arise in East Germany, the

only exceptions being Berlin and one neighbouring region. On average, East German regions do

poorly as regards diversity of their R&D staff, most notably the rural peripheral areas.

5 Econometric issues

We apply the knowledge production function introduced by Griliches (1979) to investigate the

Table 1. Cultural diversity of the labour force (Theil index), 2000

Total employment R&D employment

All skill groups High skilled All skill groups High skilled

Mean 0.35 0.22 0.15 0.18

Standard deviation 0.21 0.11 0.10 0.11

Maximum 0.86 0.47 0.47 0.55

Minimum 0.03 0.03 0.01 0.00

Agglomerated regions 0.57 0.33 0.28 0.31

Urbanized regions 0.35 0.22 0.16 0.20

Rural regions 0.26 0.16 0.15 0.16

East Germany 0.15 0.13 0.09 0.11

West Germany 0.54 0.34 0.26 0.30

Germany 0.47 0.29 0.23 0.27

570 A. Niebuhr


9/24investigate the effects of R&D inputs on regional levels of innovation (e.g., Acs et al. 1994,

Anselin et al. 1997). Referring to the theoretical outline in Section 2, if (∂Pi/∂divi) > 0, diversity

Fig. 1. Regional disparities in cultural diversity of R&D employment (Theil index), 2000



10/24

backgrounds. In contrast, (∂ Pi / ∂ divi) < 0 implies that production complementarities are too weak to compensate for the negative effects associated with cultural diversity. We check whether

positive or negative effects dominate in the regression analysis.

5.1 Basic specification

The regression analysis is applied to the knowledge production function that links R&D inputs

to innovation output, namely, patent applications. Since the number of patents is affected by the

size of the regional economy, we investigate the relationship between patents and R&D input in

per capita terms. R&D staff and R&D expenditure per inhabitant are used as proxies for R&D

activity. The basic regression model is given by:

ln ln ln lnP RD HC DIV C uit it it it k kit

k

K

it = + + + + +−=

∑α α α α β 0 1 1 2 31

(7)

where Pit is the number of patents per capita in region i and year t . RDit -1 is R&D personnel or

R&D expenditure per capita in year t-1 and uit is the error term. In order to appropriately model

the relationship between R&D input and output, the input variable enters into the model with a

time lag of one year. Patents as well as R&D input refer to firms only. We assume that knowledge

is embodied in human capital ( HC it ) and use the share of high skilled employees (university

degree) in total employment as a proxy for the regional knowledge stock. However, human

capital might also foster the innovation process via facilitating knowledge spillovers. Finally, the

inclusion of a human capital variable enables us to check whether diversity among highly

qualified workers just works as an approximation of the human capital endowment of the region.

With respect to the objective of the investigation, the coefficient of interest is a 3 that captures theimpact of cultural diversity of R&D workers on patent applications. The diversity index DIV it is

calculated according to Equations (4) to (6), depending on the considered measure. Separate

models are estimated for diversity measures based on total R&D employment and high skilled

R&D employment.10

Furthermore, we expand the original knowledge production function by some control

variables C kit in order to avoid misspecification due to omitted variables. Controls comprise an

indicator for the sectoral composition of regional economies, more precisely the ratio of

manufacturing to service employment in the region STRUC it . The industry structure is consid-

ered because the propensity to patent is higher in manufacturing than in the service sector.

According to Bode (2004), the propensity to patent might also be affected by the size of firms.In order to capture corresponding effects, two additional variables are considered: the employ-

ment shares of small (less than 20 employees) and large (500 or more employees) firms

(SMALLit , LARGE it ). As the innovation process in highly agglomerated areas may significantly

differ from the process in rural peripheral regions, we take into account the region type as well

by means of a categorical variable REGTYPE i that differentiates between agglomerated, urban-

ized and rural (see Appendix for details). Finally, we allow for systematic differences in R&D

activity between East and West Germany ( Dummy EAST ) that might for example, be due to

disadvantages caused by some kind of heritage of the centrally planned socialist economy still

at work.

10 We also considered local university research as a potential determinant of R&D output and included R&D staff atuniversities and polytechnics per inhabitant as an explanatory variable. However, results are rather disappointing since

572 A. Niebuhr


11/24

5.2 Robustness checks

To investigate the robustness of our empirical results, a number of additional regression models

are applied. First, we have to consider potential effects of measurement errors and simultaneity.

Due to endogeneity of explanatory variables, the relationship estimated by ordinary least

squares (OLS) cannot be interpreted as causal. The estimated effect of diversity on R&D outputmight be biased due to potential endogeneity of cultural diversity. Innovation activity may

influence labour migration and thus, cultural diversity of the labour force since regions charac-

terized by high innovation output, growth, and favourable labour market conditions are preferred

destinations of immigrants. We use diversity measures calculated with low skilled employment

lagged both in space and time (by 5 years) and the latitude of the region centre as instruments

( Z it ) for contemporaneous diversity indices. As an alternative instrument, we use the lagged

share of foreigners in low skilled employment. The variables are valid instruments if they are

relevant [corr( Z it , DIV it ) 0] and uncorrelated with the errors term [corr( Z it ,uit ) = 0].11

Lagged cultural diversity in neighbouring regions is supposed to influence current diversity in

the region under consideration due to migration flows. As migration occurs primarily acrossshorter distances and is influenced by herd behaviour (see Bauer et al. 2002; Burkert et al. 2008),

cultural diversity in a region is likely to affect future diversity in neighbouring areas. Moreover,

diversity among low skilled workers should reflect the existence of amenities that will also attract

high skilled workers. However, employees without a formal vocational qualification should not

play an important role in innovation. The latitude is used as an approximation of the main

destinations of immigrants in Germany in the 1950s and 1960s that are located in the Southwest

of the country. Relevance of both instruments rests on the idea that migrants limit their destination

choices to places with significant prior immigration from the same origin. The instrument

variables (IV) are highly correlated with the diversity measures and unlikely to be affected by

reverse causation. This applies especially to the latitude variable as a pure geographic variable.Second, fixed and random effects panel data models are applied so as to control for

unobserved time-invariant effects:

ln ln ln lnP RD HC DIV C it it it it k kit k

K

i t = + + + + + + +−=

∑α α α α β η λ ν 0 1 1 2 31

iit (8)

where hi denotes a region-specific effect, controlling for unobserved regional characteristics, l t captures time effects, and n it is a white noise error term.

Moreover, evidence provided by Bode (2004) and Anselin et al. (1997) suggest that geo-

graphically bounded spillovers and spatial dependence are important for regional innovationactivity. Therefore, we check for misspecification due to omitted spatial effects indicated by

spatial autocorrelation in the regression residuals. Depending on the results of corresponding

LM-tests, we might estimate spatial lag models or spatial error models. The spatial lag model is

given by:

ln ln ln ln lnP w P RD HC DIV C it ij jt j

R

it it it k k = + + + + +=

−∑α ρ α α α β 01

1 1 2 3 iit

k

K

it u=

∑ +1

(9)

Thus, we extend the non-spatial model by a spatial lag of the dependent variable

w Pij jt j

R

ln=

∑1

where wij is an element of the R ¥ R spatial weights matrix W. We consider two



12/24

alternative ‘raw’ adjacency matrices W in this analysis. A first and frequently applied specifi-

cation is a binary spatial weights matrix such that wij = 1 if the regions i and j share a border andw

ij = 0 otherwise. Second, wij is set to the inverse of travel time between the capitals of regionsi and j with a cut-off point at 150 km, i.e. wij = 0 if distance between region’s centres exceedsthis cut-off point. From the raw matrices W spatial weights matrices W are derived by

normalizing such that Σ j ijw i= ∀1, .Taking into account the weighted sum of patent applications in neighbouring regions implies

that spatial autocorrelation of the error term is due to omission of some substantive form

of spatial dependence caused by interaction among regions. We assume that there might be

important knowledge spillovers that result in interdependent innovation processes of adjacent

R&D departments leading to spatial dependence in patent applications. Localized knowledge

spillovers are likely to be caused by interregional mobility of R&D workers which in turn is

affected by frictional effects of distance. Moreover, evidence in Scherngell and Barber (2009)

suggests that geographical distance and co-localization of organizations in neighbouring regions

are important determinants of R&D collaborations. Following Bode (2004), we apply the

patents granted to researchers in neighbouring regions as a proxy for the knowledge stock inadjacent locations.

In contrast, the spatial error model will be the appropriate specification if the misspecifica-

tion is due to nuisance dependence. Spatial autocorrelation in measurement errors or in variables

that are otherwise not crucial to the model might entail spatial error dependence. The spatial

error model may be expressed as:

ln ln ln lnP RD HC DIV C uit it it it k kit k

K

it = + + + + +−=

∑α α α α β 0 1 1 2 31

(10)

with u w uit ij jt it j

R

= +=

∑λ ε 1

as the spatial autoregressive error term.

Finally, we take into account that outlying observations might have a marked effect on the

regression results. To address this issue, we apply quantile regressions as introduced by Koenker

and Basset (1978). The median regression corresponds to the least absolute deviation estimator

and is a robust alternative to OLS. Quantile regressions minimize an objective function which

is a weighted sum of absolute deviations:

min: :γ γ γ

θ γ θ γ y x y x i i

i y x i i

i y x i i i i

− ′ + −( ) − ′

≥ ′ < ′∑ ∑ 1 (11)

Here yi is the dependent variable and x i is the vector of explanatory variables which is multiplied

by the coefficient g . The objective function can be interpreted as an asymmetric linear penaltyfunction of deviations from predicted to actual patents per capita. An important special case is

the median regression (q = 0.5). Since this regression puts less weight on outliers than OLS, itis a robust alternative.

6 Regression results and discussion

A point of departure of the regression analysis is a basic pooled model that is estimated with

574 A. Niebuhr


13/24

4 to total R&D employment ( DIV it ) and high skilled R&D staff ( DIV it high). Table 2 shows the

results of the basic regression model. The specifications in columns 1 to 6 only differ with

respect to the diversity measure included. In line with previous evidence on the knowledge

production function, we get a highly significant impact of R&D expenditure on innovation

output.12 A positive effect is also associated with the region’s human capital endowment.

However, the coefficient is only marginally significant at the 10% level in the models 2 and 4,those specifications including diversity among high skilled R&D staff only.

Furthermore, some control variables appear with significant coefficients, indicating that

structural characteristics of the regions matter for innovation activity. The relative size of the

industrial sector is associated with a significant effect on innovation output. According to the

estimates, a specialization of regions on manufacturing as compared to services tends to raise

patents per capita. Furthermore, areas characterized by a relatively large share of small firms on

average seem to perform better than other regions. The negative coefficient of the region type

variable implies that there might be systematic differences between the innovation processes of

metropolitan areas, urbanized and rural regions. More precisely, less densely populated regions,

especially rural areas, tend to be marked ceteris paribus by lower productivity of R&D activity.This might point to some kind of positive agglomeration effect at work. However, the region

type variable is marginally significant in some specifications only. Moreover, the significant

coefficient of the dummy variable suggests that there are still systematic differences in R&D

activity between East and West Germany. Even after controlling for R&D input and other factors

that influence regional patent applications East German regions achieve a lower R&D output per

capita than locations in the western part of the country.

Finally, turning to the most important variable, the results point to an innovation-enhancing

effect of cultural diversity of the workforce. The coefficient of cultural diversity is positive and

highly significant, irrespective of applied diversity measure. Only in model 6, is diversity not

significant at the 5% level. Thus, the impact of diversity is rather robust with respect to avariation of measurement. Differences in size of the coefficients, that show up especially for the

Krugman index on the one hand and the Theil and Herfindahl measures on the other hand, can

be traced back to different definitions of the indices. Further, the impact of diversity among

highly educated R&D employees is smaller than the effect that is detected for total R&D staff.

Thus, the regression results indicate that cultural diversity is a factor which positively influences

the process of knowledge creation. The qualification level of labour might not matter in this

context because the educational attainment of R&D workers and their communication skills

tend to be on average relatively high.

However, as indicated by the tests for spatial autocorrelation (Moran’s I , Lagrange multiplier

error and Lagrange multiplier lag tests), regional R&D activity is marked by some spatialinteraction not captured by the regression model so far. The differences between the test

statistics suggest that problems are caused by omission of some kind of substantive form of

spatial dependence that might rest upon knowledge spillovers between neighbouring regions.13

In order to check whether the identified impact of cultural diversity is affected by the omission

of spatial dependence, we include a spatial lag of patent applications per capita in some

specifications. Moreover, the results of the Hausmann-Wu test for endogeneity of the diversity

measures point to a potential problem that might adversely affect the estimates. However, for

diversity of total R&D employment the test is only significant at the 5% level. Nevertheless, we

apply instrument variable (IV) regression in order to check the robustness of results with respect

to potential endogeneity of our diversity variables.



14/24

T a b l e 2 . R e g r e s s i o n r e s u l t s f o r a l t e r n a t i v e d i v e r s i t y m e a s u r e s ( P o o l e d O L S )

e n t v a r i a b l e

l n ( p a t e n t s p e r c a p i t a )

T h e i l

I n d e x

H e r fi n d a h l I n d e x

K r u g m a n I n d e x

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

n t

2 . 6 9 * * ( 2 . 8 3 )

2 . 6 6 * * ( 2 . 8 1 )

2 . 8 3 * * ( 2 . 9 3 )

2 . 8 5 * * ( 2 . 9 8 )

8 . 0 8 * * ( 2 . 7 9 )

5 . 3 6 * ( 2 . 4 8 )

1 )

0 . 3 5 * * ( 7 . 6 6 )

0 . 3 6 * * ( 7 . 9 3 )

0 . 3 6 * * ( 7 . 7 3 )

0 . 3 6 * * ( 7 . 9 1 )

0 . 3 8 * * ( 8 . 2 8 )

0 . 3 9 * * ( 8 . 4 5 )

)

0 . 2 5 ( 1 . 5 0 )

0 . 3 1 ( 1 . 9 2 )

0 . 2 6 ( 1 . 6 1 )

0 . 3 2 ( 1 . 9 6 )

0 . 1 7 ( 0 . 9 1 )

0 . 2 5 ( 1 . 4 7 )

t)

0 . 2 4 * * ( 3 . 4 0 )

0 . 1 9 * * ( 2 . 9 3 )

9 . 2 2 * ( 2 . 1 3 )

t) h i g h

0 . 1 9 * * ( 3 . 1 8 )

0 . 1 6 * * ( 3 . 0 6 )

4 . 8 4 ( 1 . 5 4 )

U C

i t )

0 . 6 9 * * ( 8 . 6 9 )

0 . 7 1 * * ( 9 . 0 6 )

0 . 6 9 * * ( 8 . 6 6 )

0 . 7 2 * * ( 9 . 0 8 )

0 . 6 8 * * ( 8 . 4 2 )

0 . 6 9 * * ( 8 . 5 7 )

L L

i t )

0 . 8 0 * * ( 2 . 9 5 )

0 . 8 0 * * ( 2 . 8 0 )

0 . 8 0 * * ( 2 . 9 0 )

0 . 7 9 * * ( 2 . 7 6 )

0 . 8 9 * * ( 3 . 0 4 )

0 . 9 1 * * ( 3 . 0 0 )

G E

i t )

0 . 1 3 ( 1 . 0 5 )

0 . 0 9 ( 0 . 6 6 )

0 . 1 3 ( 1 . 0 0 )

0 . 0 9 ( 0 . 6 8 )

0 . 1 6 ( 1 . 2 2 )

0 . 1 5 ( 1 . 1 4 )

YP

E i

- 0 . 0 6 ( 1 . 3 4 )

- 0 . 0 7 ( 1 . 6 1 )

- 0 . 0 7 ( 1 . 6 2 )

- 0 . 0 8 ( 1 . 7 9 )

- 0 . 0 9 * ( 2 . 1 0 )

- 0 . 1 0 * ( 2 . 2 0 )

E A S T

- 0 . 3 5 * ( 2 . 0 7 )

- 0 . 4 7 * * ( 3 . 2 1 )

- 0 . 4 1 * ( 2 . 5 1 )

- 0 . 4 8 * * ( 3 . 3 5 )

- 0 . 5 1 * * ( 3 . 0 5 )

- 0 . 6 1 * * ( 4 . 0 6 )

0 . 8 7

0 . 8 7

0 . 8 7

0 . 8 7

0 . 8 7

0 . 8 7

a t i o n s

1 9 0

1 8 9

1 9 0

1 8 9

1 9 0

1 9 0

u s m a n F

- T e s t

6 . 2 7 *

7 . 9 5 * *

6 . 6 9 *

7 . 7 8 * *

4 . 9 9 *

5 . 7 6 *

s I

1 . 5 9

2 . 0 5 *

1 . 7 3

2 . 0 8 *

2 . 0 4 *

2 . 1 8 *

o r

0 . 6 1

1 . 4 5

0 . 8 0

1 . 5 0

1 . 3 5

1 . 7 0

L M e r r o r

0 . 0 5

0 . 0 1

0 . 0 2

0 . 0 1

0 . 0 0

0 . 0 0

4 . 8 6 *

6 . 7 2 * *

5 . 2 1 *

6 . 5 7 * *

6 . 0 2 *

8 . 1 5 * *

L M l a g

4 . 3 0 *

5 . 2 8 *

4 . 4 3 *

5 . 0 8 *

4 . 6 7 *

6 . 4 5 *

t - s t a t i s t i c s i n p a r e n t h e s e s a r e b a s e d u p o n H u b

e r - W h i t e s t a n d a r d e r r o r s . * * s i g n i fi c a n t a t t h e 0 . 0 1 l e v e l , * s i g n i fi c a n t a t t h e 0 . 0 5 l e v e l .

d e l s i n c l u d e t i m e fi x e d e f f e c t s . T e s t o n s p a t i a l

a u t o c o r r e l a t i o n w e r e c o n d u c t e d w i t h

d i f f e r e n t w e i g h t m a t r i c e s i n o r d e r t o

c h e c k r o b u s t n e s s .

u l t s p r e s e n t e d i n t h e t a b l e a r e b a s e d o n a w e i g h t s m a t r i x u s i n g i n v e r s e d i s t a n c e w i t h a c u t - o f f p o i n t o f 1 5 0 k m .

576 A. Niebuhr


15/24

The results of the IV regressions indicate that endogeneity of cultural diversity is unlikely to

be a major problem (see Table 3). The diversity measures are instrumented by diversity mea-

sures calculated with low skilled employment lagged both in space and time, lagged shares of

foreigners in low skilled employment, and the latitude of the region. In the following, we focus

on results for Theil diversity measures only. The impact of cultural diversity on innovation

output is even reinforced in the IV regressions. All IV estimates are positive, significant, and

larger than their OLS counterparts. This is surprising since simultaneity should result in upward

biased OLS estimates of the impact of cultural diversity. This suggests that the simultaneity bias

in the OLS estimates is relatively small. The gap between OLS and IV estimates might reflect

a downward bias in the OLS estimates caused by measurement errors. This may indicate that

measurement error’s bias towards zero is more important than the upward bias due to the

endogenous location choice of immigrants. Hunt and Gauthier-Loiselle (2008) provide similar

evidence for US States. Another explanation is that there is heterogeneity in the impact of diversity on innovation output, and that the IV estimates tend to recover effects for a subset of

regions with relatively strong impact of diversity on patent intensity.14

The quality of the IV results critically hinges on the quality of the instruments, i.e. whether

they are relevant and exogenous. We apply the test of overidentifying restrictions to check

instrument exogeneity. The results of the Hansen J-statistic suggest that we can not reject the

hypothesis that the instruments are exogenous. The F-statistics of excluded instruments as well

as Shea’s Partial R2 indicate that the partial correlation between the instruments and the

endogenous explanatory variables is sufficient to ensure unbiased estimates and relatively small

standard errors.15

14 See Card (2001) for a corresponding reasoning with respect to returns to schooling.

Table 3. IV regression (2SLS)

Dependent variable ln(patents per capita)

(1) (2) (3) (4)

Constant 3.11** (3.00) 3.25** (2.87) 3.11** (3.01) 3.26** (2.86)

ln( RDit-1) 0.28** (4.74) 0.25** (3.83) 0.28** (4.70) 0.25** (3.75)

ln( HC it ) 0.12 (0.63) 0.25 (1.22) 0.12 (0.62) 0.25 (1.20)

ln( DIV_T it ) 0.63** (3.37) 0.63** (3.35)

ln( DIV_T it ) high 0.71** (3.27) 0.72** (3.31)

ln(STRUC it ) 0.69** (8.02) 0.77** (7.50) 0.69** (7.99) 0.78** (7.44)

ln(SMALLit ) 0.51* (2.35) 0.30 (1.13) 0.50* (2.30) 0.29 (1.05)

ln( LARGE it ) 0.07 (0.55) -0.14 (0.97) 0.06 (0.53) -0.14 (1.00)

REGTYPE i 0.001 (0.02) -0.002 (0.02) 0.002 (0.04) 0.001 (0.01)

Dummy EAST 0.24 (0.73) 0.28 (0.76) 0.25 (0.75) 0.30 (0.82)

Hansen J statistica 1.12 (0.2893) 0.46 (0.4983) 0.17 (0.6813) 0.15 (0.7017)

Shea Partial R2 0.20 0.11 0.20 0.10

F-Test of excluded instruments 21.71** 10.62** 22.63** 10.46**

Notes: t -statistics in parentheses are based upon Huber-White standard errors.

** significant at the 0.01 level, * significant at the 0.05 level. All models include time fixed effects.

Latitude of region centre, diversity measures (Krugman index) lagged in space and time are used as instruments in

models 1 and 2. Latitude and the lagged share of low skilled foreigners in total employment are applied as instruments

in the models 3 and 4.a p-value in parentheses.



16/24

Before turning to the significance of spatial interaction, we check whether unobserved

region-specific effects are important and adversely affect the estimates of the pooled model. In

Table 4 the results of the random and fixed effects models as well as the between estimator are

summarized. The results of the random effects models (columns 1 and 2) are very similar to the

estimates of the OLS regression of the pooled data. The coefficient of R&D expenditure slightly

declines but is still highly significant. The impact of cultural diversity turns out to be very stableas well. The effect of diversity of total R&D employment is even reinforced. However, the

findings change dramatically in the fixed effects models (columns 3 and 4). The coefficients of

the diversity measures are insignificant, although still of the same sign at least for total R&D

employment.16 In contrast, applying an alternative specification with fixed effects on the NUTS

1 level, namely for those German Länder that consist of several planning regions, does not

significantly change the results with respect to cultural diversity. This additional robustness

check is conducted for the specification including the Theil index. The coefficients of the

diversity measures slightly decline from 0.24 to 0.21 for ln( DIV_T it ) and from 0.19 to 0.16 for

ln( DIV_T it ) high. Furthermore, the effect of cultural diversity is still significant at the 5% level

in both specifications.The problem of the fixed effects model might be linked to the quality of the data on R&D

input, survey data that may be affected by measurement errors. Johnston and DiNardo (1997)

note that estimates may be biased towards zero due to mismeasurement of explanatory variables.

The attenuation bias can be aggravated by fixed effects estimation, in particular if the explana-

tory variables are highly correlated across time, as is frequently the case when the time period

between the two cross sections is small (see also Griliches and Hausman 1986). With respect to

the data set used in the regression analysis, this applies to R&D expenditure per capita as well

as to the diversity indices. Although there is a considerable variation across regions, there is

much less variation in time.17 This is confirmed by the results of the between estimator (columns

5 and 6) which only uses variation between cross sectional units by applying OLS to thetime-averaged Equation (7). The random effects estimators is the weighted average of the fixed

effects and the between estimator. Differences between random effects and between estimator

results are, however, fairly small. Because of the completely implausible implications and the

outlined methodological problems of the fixed effects specification, we rely on the random

effects model that confirms the OLS and IV results.

We also check whether spatial interaction affects the estimates by applying spatial regression

models. The results in Table 5 suggest that there are significant spillover effects among neigh-

bouring regions. The coefficient of the spatially lagged dependent variable is significantly

different from zero in almost all models, the only exceptions being the two stage least square

(2SLS) estimates in column 6. Thus, the neighbourhood of innovative regions promotes regionalR&D activity. This confirms the evidence on localized knowledge spillovers provided in

Bottazzi and Peri (2003) and Bode (2004). The result is robust with respect to a variation of the

spatial weights matrix (binary contiguity, inverse distance). However, the inclusion of spatially

lagged patents per capita does not seriously affect the coefficient of cultural diversity. The main

differences regarding the impact of diversity emerge between the maximum likelihood (ML)

estimates and the 2SLS results. The size of the coefficient increases once we instrument cultural

diversity in the 2SLS regressions. This is in line with IV results summarized in Table 3.

Altogether, the basic findings regarding the impact of cultural diversity on innovation are not

changed in the spatial lag models.

16 This is also in line with evidence surveyed in Griliches (1990). Several studies indicate that patents are more

578 A. Niebuhr


17/24

T a b l e 4 . P a n e l m o d e l s

e n t

e


R a n d o m e f f e c t s

F i x e d e f f e c t s

B e t w e e n r e g r e s s i o n

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

n t

4 . 0 2 * * ( 3 . 3 4 )

3 . 9 0 * * ( 3 . 2 0 )

6 . 3 4 * ( 2 . 3 7 )

5 . 9 6 * ( 2 . 3 2 )

2 . 7 3 ( 1 . 8 4 )

1 . 9 7 ( 1 . 2 9 )

1 )

0 . 2 2 * * ( 3 . 5 6 )

0 . 2 4 * * ( 3 . 9 4 )

- 0 . 2 1 ( 1 . 8 7 )

- 0 . 2 2 ( 1 . 8 7 )

0 . 3 9 * * ( 6 . 0 5 )

0 . 3 9 * * ( 6 . 1 9 )

0 . 4 0 ( 1 . 8 4 )

0 . 5 0 * ( 2 . 3 0 )

- 0 . 5 2 ( 0 . 7 0 )

- 0 . 3 6 ( 0 . 5 3 )

0 . 2 3 ( 0 . 9 7 )

0 . 2 6 ( 1 . 1 1 )

_ T

i t )

0 . 3 0 * * ( 3 . 5 5 )

0 . 2 0 ( 1 . 1 6 )

0 . 2 1 * ( 2 . 0 5 )

_ T

i t ) h i g h

0 . 1 5 * ( 2 . 0 4 )

- 0 . 0 7 ( 0 . 5 8 )

0 . 1 7 ( 1 . 8 6 )

U C

i t )

0 . 8 5 * * ( 7 . 4 2 )

0 . 8 7 * * ( 7 . 5 0 )

1 . 0 9 ( 1 . 6 9 )

0 . 9 3 ( 1 . 4 8 )

0 . 6 6 * * ( 5 . 6 4 )

0 . 6 7 * * ( 5 . 7 0 )

L L

i t )

0 . 3 5 ( 1 . 4 2 )

0 . 4 0 ( 1 . 5 8 )

0 . 0 8 ( 1 . 3 4 )

0 . 1 3 ( 1 . 9 1 )

1 . 0 6 * ( 2 . 5 1 )

1 . 0 7 * ( 2 . 5 2 )

G E

i t )

0 . 1 1 ( 0 . 8 4 )

0 . 0 7 ( 0 . 5 5 )

0 . 7 8 * ( 2 . 0 6 )

0 . 6 4 ( 1 . 8 9 )

0 . 1 8 ( 1 . 0 9 )

0 . 1 6 ( 0 . 9 2 )

P E

i

- 0 . 0 5 ( 0 . 8 8 )

- 0 . 0 8 ( 1 . 3 8 )

- 0 . 0 6 ( 1 . 0 4 )

- 0 . 0 7 ( 1 . 1 8 )

E A S T

- 0 . 3 4 ( 1 . 7 4 )

- 0 . 6 2 * * ( 3 . 3 3 )

- 0 . 3 8 ( 1 . 6 8 )

- 0 . 4 5 * ( 2 . 2 6 )

i n

0 . 3 5

0 . 2 9

0 . 5 3

0 . 5 1

0 . 0 0

0 . 3 9

e e n

0 . 8 8

0 . 8 8

0 . 4 5

0 . 2 1

0 . 9 0

0 . 9 0

a l l

0 . 8 7

0 . 8 6

0 . 4 5

0 . 2 2

0 . 8 7

0 . 6 3

a t i o n s

1 9 0

1 8 9

1 9 0

1 8 9

1 9 0

1 8 9

t - s t a t i s t i

c s i n p a r e n t h e s e s a r e b a s e d u p o n H u b

e r - W h i t e s t a n d a r d e r r o r s . * * s i g n i fi c a n t a t t h e 0 . 0 1 l e v e l , * s i g n i fi c a n t a t t

h e 0 . 0 5 l e v e l .

d e l s i n c l u d e t i m e fi x e d e f f e c t s .



18/24

T a b l e 5 . S p a t i a l l a g m o d e l s

e n t v a r i a b l e


M L ( b i n a r y )

M L ( i n v e r s e d i s t a n c e )

2 S L S ( i n v e r s e

d i s t a n c e )

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

n t

1 . 4 1 ( 1 . 3 9

)

1 . 3 1 ( 1 . 3 0 )

1 . 1 4 ( 1 . 1 3 )

1 . 0 6 ( 1 . 0

6 )

2 . 8 9 * * ( 3 . 1 5 )

3 . 2 3 * * ( 3 . 1 3 )

1 )

0 . 3 5 * * ( 7 . 9 6 )

0 . 3 6 * * ( 8 . 3 9 )

0 . 3 5 * * ( 8 . 0 2 )

0 . 3 6 * * ( 8 . 4 6 )

0 . 3 0 * * ( 6 . 0 1 )

0 . 2 6 * * ( 4 . 4 2 )

)

0 . 1 6 ( 1 . 0 1

)

0 . 2 1 ( 1 . 3 3 )

0 . 1 7 ( 1 . 1 1 )

0 . 2 1 ( 1 . 4

1 )

0 . 1 5 ( 0 . 8 5 )

0 . 2 9 ( 1 . 5 3 )

_ T

i t )

0 . 2 1 * * ( 2 . 9 4 )

0 . 1 9 * * ( 2 . 5 6 )

0 . 5 1 * * ( 2 . 9 3 )

_ T

i t ) h i g h

0 . 1 7 * * ( 3 . 0 7 )

0 . 1 5 * * ( 2 . 6 8 )

0 . 6 5 * * ( 3 . 2 9 )

U C

i t )

0 . 5 4 * * ( 5 . 9 9 )

0 . 5 5 * * ( 6 . 1 3 )

0 . 4 9 * * ( 5 . 0 1 )

0 . 5 0 * * ( 5 . 1 1 )

0 . 6 1 * * ( 7 . 3 3 )

0 . 7 2 * * ( 7 . 1 2 )

L L

i t )

0 . 5 5 * ( 2 . 1

5 )

0 . 5 3 * ( 2 . 0 2 )

0 . 5 1 * ( 2 . 1 6 )

0 . 5 0 * ( 2 . 0 5 )

0 . 8 1 * * ( 2 . 6 7 )

0 . 5 2 ( 1 . 6 6 )

G E

i t )

0 . 0 6 ( 0 . 5 2

)

0 . 0 2 ( 0 . 1 8 )

0 . 0 3 ( 0 . 2 6 )

- 0 . 0 1 ( 0 . 0

4 )

0 . 1 2 ( 0 . 9 9 )

- 0 . 1 1 ( 0 . 7 1 )

YP

E i

- 0 . 0 7 ( 1 . 6 1

)

- 0 . 0 8 ( 1 . 8 4 )

- 0 . 0 8 * ( 1 . 9 8 )

- 0 . 0 9 * ( 2 . 1 9 )

0 . 0 2 ( 0 . 3 8 )

0 . 0 2 ( 0 . 2 8 )

E A S T

- 0 . 3 5 * ( 2 . 0

5 )

- 0 . 4 3 * * ( 2 . 9 9 )

- 0 . 4 3 * * ( 2 . 6 7 )

- 0 . 5 1 * * ( 3 . 7 6 )

0 . 1 6 ( 0 . 5 1 )

0 . 2 4 ( 0 . 7 4 )

P i t ) ( r )

0 . 1 5 * * ( 2 . 8 1 )

0 . 1 6 * * ( 2 . 9 6 )

0 . 2 0 * * ( 3 . 0 7 )

0 . 2 1 * * ( 3 . 1 8 )

0 . 4 7 * ( 2 . 0 2 )

0 . 3 5 ( 1 . 3 6 )

a r t i a l R 2

n ( P

i t )

0 . 3 5

0 . 3 3

I V_

T i t )

0 . 2 0

0 . 1 1

o f e x c l u d e d i n s t r u m e n t s

n ( P

i t )

3 2 . 6 * *

3 1 . 8 * *

I V_

T i t )

1 4 . 7 * *

8 . 3 * *

u s m a n F

- T e s t

1 . 8 5

3 . 7 3 *

t - s t a t i s t i c s i n p a r e n t h e s e s a r e b a s e d u p o n H u b e

r - W h i t e s t a n d a r d e r r o r s ; * * s i g n i fi c a n

t a t t h e 0 . 0 1 l e v e l , * s i g n i fi c a n t a t t h e 0 . 0 5 l e v e l . A l l m o d e l s i n c l u d e t i m e fi x e d e f f e c t s . L a t i t u d e

n c e n t r e

, d i v e r s i t y m e a s u r e s ( K r u g m a n i n d e x

) l a g g e d i n s p a c e a n d t i m e a r e u s e d a s

i n s t r u m e n t s f o r d i v e r s i t y m e a s u r e s i n

t h e m o d e l s 5 a n d 6 . P a t e n t a p p l i c a t i o

n s l a g g e d i n s p a c e

e i s u s e d a s a n i n s t r u m e n t f o r t h e s p a t i a l l a g o f t h e e n d o g e n o u s v a r i a b l e .

u l t s p r e s e n t e d i n t h e t a b l e a r e b a s e d o n a b i n a r y w e i g h t s m a t r i x a n d a m a t r i x u s i n g

i n v e r s e d i s t a n c e w i t h a c u t - o f f p o i n t

o f 1 5 0 k m .

580 A. Niebuhr


19/24

Furthermore, we investigate whether outlying observations affect the estimates by applying

quantile regressions. Table 6 shows results for the basic specification and spatial lag models. The

coefficients of the median regression are rather similar to the previous estimates for the corre-

sponding diversity measures in Tables 2 and 5, indicating that the effect of cultural diversity is

not subject to serious bias caused by outliers. Significance levels of the coefficients decline, but

the impact of cultural diversity of total R&D employment is still significant at the 10% and 5%

level (columns 1 and 3). However, as regards diversity among high skilled R&D staff, we detectan important impact in the non-spatial model only.

Finally, we turn to the size of the effect of cultural diversity focusing on the evidence for total

R&D employment since these results seem to be more robust than those for high skilled R&D

workers. Total R&D employment in Germany amounted to roughly 1,971,000 employees in the

year 2000. The share of foreign workers in R&D employment is 2.9% (58,000 foreign employ-

ees) and corresponds to a Theil index of 0.23. If the number of foreign R&D workers grows by

100%, and the increase of all nationalities is proportional to their size, the Theil index will rise

to 0.41. Applying the lowest coefficient estimates (ML spatial lag model: 0.19), this change in

cultural diversity of R&D employment will cause an increase of patents per capita by almost

12%. In contrast, the IV estimate of the spatial lag model (0.51) gives rise to an increase of

patents per capita by 34% (direct effect).18 Thus, there is a significant im

migration and innovation

Documents