The Nexus between Labor Diversity

and Firm's Innovation

Pierpaolo Parrotta, Dario Pozzoli and Mariola Pytlikova

NORFACE MIGRATION Discussion Paper No. 2011-5

www.norface-migration.org

The Nexus between Labor Diversity andFirm's Innovation∗

Pierpaolo Parrotta†, Dario Pozzoli‡and Mariola Pytlikova§

January 6, 2011

Abstract

In this paper we investigate the nexus between �rm labor diversity and in-

novation using a linked employer-employee data from Denmark. Speci�cally,

exploiting information retrieved from the comprehensive database and imple-

menting a proper instrumental variable strategy, we are able to identify the

contribution of workers diversity in cultural background, skills and demographic

characteristics to valuable �rm's innovation activity. The latter is measured

by: (1) the �rm's propensity to apply for a patent, (2) the number of patent

applications (intensive margin) and (3) the �rm's ability to patent in di�erent

technological areas (extensive margin). We �nd that skill and ethnic diversity

plays an important role in propelling �rm's innovation outcomes. Conversely,

the e�ect of demographic diversity typically vanishes once detailed �rm-speci�c

characteristics are included as control variables.

JEL Classi�cation: C23, J24, L20.

Keywords: Labor diversity, patenting activity, extensive and intensive margins.

∗We thank Guglielmo Barone, Tor Eriksson, Michael Rosholm, and Måns Soderbom (al-phabetical order) for helpful suggestions. In addition, we appreciate comments from partic-ipants at seminars organized by the Copenhagen Business School, University of Bergamo,Aarhus School of Business, University of Lausanne, and from participants at the followingconferences: ESPE 2010, The 5th Nordic Summer Institute in Labor Economics, The 2010Ratio Young Scientist Colloquium, ESEM 2010, CAED/COST 2010 in London and the 2010International Symposium on Contemporary Labor Economics at WISE, Xiamen. MariolaPytlikova gratefully acknowledges funding from the NORFACE Migration Programme �Mi-gration: Integration, Impact and Interaction�. The usual disclaimer applies.†Aarhus School of Business and Social Sciences, Aarhus University.‡Aarhus School of Business and Social Sciences, Aarhus University.§Corresponding author. Aarhus School of Business and Social Sciences, Aarhus Uni-

versity. Department of Economics, Hermodsvej 22, DK, 8230 Aabyhoj, Denmark. E-mail:marp@asb.dk

1

1 Introduction

Many developed and developing countries have experienced several changes

in the workforce composition which has lead to an increased heterogeneity of

the labor force in terms of age, gender, skills and ethnicity. This is partly the

result of policies adopted to counteract the problem of population aging, anti-

discrimination measures, immigration and the worldwide globalization process

(Pedersen, Pytlikova and Smith, 2008). From the demand side, we observe

increasing diversity across many workplaces and we often hear about the im-

portance of further internationalization and demographic diversi�cation. The

promotion of diversity is often perceived as a chance to improve learning and

knowledge management capabilities and then enhance �rm productivity (Par-

rotta, Pozzoli and Pytlikova, 2010). Besides, workforce diversity is believed to

be an important source of innovation. For instance, in a relatively recent sur-

vey conducted by the European Commission, a large number of respondents

identi�ed innovation as a key bene�t of having diversity policies and practices

(European Commission, 2005). If this is the case, �rms could bene�t from the

growing diverse cultural backgrounds, demographic, and knowledge bases of the

workforces. Moreover, since there is a widespread consensus that innovation

is crucial for sustainable growth and economic development (the new growth

theory), knowing the link between workforce diversity and innovation seems to

be essential for policy makers.

There are numerous, predominantly management studies focusing on the

relationship between labor diversity and �rm's innovation. In the literature

a paradox has been recognized: whereas labor diversity can be a source of

creativity and therefore foster innovation activity, a high degree of heterogene-

ity among workers may induce misunderstanding, con�icts and uncooperative

behaviors within workplaces and in this way hinder innovation (Basset-Jones,

2

2005). There is no general agreement on which e�ect may prevail. However, the

paradox weakens if we distinguish between cognitive and demographic diver-

sity. Speci�cally, di�erences in skills, education and more broadly in knowledge

among employees seem to be bene�cial rather than detrimental (Hong and Page,

2001 and 2004; Lazear, 1999). According to Lazear (1999), positive e�ects may

prevail as long as workers' information sets are not overlapping but relevant

to one another. Ambiguity instead persists for diversity in ethnic and demo-

graphic characteristics of employees. On the one hand, people of di�erent cul-

tural backgrounds, age and gender may provide diverse perspectives, valuable

ideas, problem-solving abilities, and in this way facilitate the achievement of

optimal creative solutions and therefore stimulate innovations (Watson, Kumar

and Michaelsen, 1993; Drach-Zahavy and Somech, 2001; Hong and Page, 2001

and 2004). People of di�erent ethnic backgrounds may also possess knowledge

about global markets and customers tastes (Osborne, 2000; Berliant and Fujita,

2008) and di�erent ways of searching for solutions to problems, i.e. heuris-

tics, and therefore they are more likely to come up with innovative solutions

than ethnically homogenous teams of workers (Hong and Page, 2004). On the

other hand, such heterogeneities might create communication barriers, reduce

the workforce cohesion and prevent cooperative participation in research ac-

tivities, bringing high costs of �cross-cultural dealing� (Williams and O'Reilly,

1998; Zajac, Golden and Shortell, 1991; Lazear, 1999). Thus, it is still unclear

whether more ethnically and demographically heterogeneous �rms outperform

the relatively more homogeneous ones with respect to innovation.

The empirical literature exploring the relationship between labor diversity

and �rm's innovation consists mainly of business case studies that often look at

work-team compositions (Horwitz and Horwitz, 2007; and Harrison and Klein,

2007) or even focus on diversity in top management teams only (Bantel and

3

Jackson, 1989; Knight, Pearce, Smith, Olian, Sims, Smith and Flood, 1999;

Pitcher and Smith, 2001)1. That may be imputed to di�erences in research

aims and approaches, but also to the lack of more comprehensive employer-

employee data, which provide a notable amount of information on the labor

force composition at the �rm level. To the best of our knowledge, the evidence

using more comprehensive data is virtually non-existent2.

In this paper, we investigate the nexus between labor diversity and innova-

tion using a rich register-based linked employer-employee dataset (LEED) from

Denmark for the years 1995-2003. Regarding measures of innovation, we fol-

low previous literature and make use of information on patents to proxy for

innovation (Griliches, 1990; Bloom and Van Reenen, 2002). Speci�cally, we

use the following three measures: (1) �rm's propensity to apply for a patent,

(2) the number of patents introduced each year and (3) technological areas in

which the �rm has realized them. We investigate the e�ect of labor diversity on

�rm innovation by looking at three dimensions of employee diversity: cultural

background, skills/education and demographics. Further, we deal with several

problems that previous literature studying the impact of workforce diversity

on innovation did not address properly. Most importantly, it might be that

�rms are aware of the importance of labor diversity and leverage it to improve

1There exists also some literature on the e�ects of diversity - typically ethnic labor diversity- on innovation using aggregate regional or industry data, for instance Kelley and Helper(1999), Feldman and Audretsch (1999), Anderson, Quigley and Wilhelmsson (2005), Niebuhr(2010); Kerr and Lincoln (2010).

2Since we began working on the paper, we became aware of two recent studies usingmore comprehensive data to analyze labor composition and innovation. The �rst work byØstergaard, Timmermarns and Kristinsson (2009) analyzes a cross-section of 1648 �rms inDenmark to evaluate the e�ect of diversity in gender, age, ethnicity and education on �rm'spropensity to innovate. They �nd evidence of (a) positive e�ects of diversity in education andgender, (b) no signi�cant e�ects of ethnic diversity and (c) negative e�ects of age diversity on�rm's innovation. The second study by Söllner (2010) examines how occupational diversity,considered as a proxy of human capital heterogeneity, a�ects the �rm's likelihood to introducea product innovation. Controlling for age and tenure diversity among other covariates, he �ndsthat �occupational diversity is positively related to the propensity to innovate�. However,both studies present some limitations, among others they neglect the problem of possibleendogeneity of the relationship between diversity and innovation, which we properly addressin the present article.

4

their performances; then the relationship under investigation may be a�ected by

simultaneity or endogeneity. To address these concerns, we implement an instru-

mental variable (IV) strategy based on levels of workforce diversity computed

for each commuting area, where a �rm is located, as an instrument for the �rm

labor diversity. Further, as broadly documented by industrial and knowledge

economics literature, �rms are characterized by a di�erent propensity to inno-

vate. Thus, there exist unobserved and observed �rm-speci�c heterogeneity that

should be taken into account to evaluate the e�ect of any labor diversity dimen-

sion on �rm's innovation outcome. Following Blundell, Gri�th and Van Reenen

(1995), we account for past �rms' success in innovation and use pre-sample infor-

mation as an observable proxy for unobservable permanent �rm characteristics.

Finally, we control for the potential role of the external knowledge in favoring

�rms' patenting activity and compute knowledge spillovers indicators based on

geographical and technological distances between �rms.

Implementing alternative estimation techniques, we �nd an evidence of the

key role of the skill diversity in promoting �rm's innovation as measured by the

probability to innovate, the number of patent applications and patent grants.

Diversity in cultural background has a positive and signi�cant in�uence, too.

E�ects of diversity in demographics turn to be mostly insigni�cant when shares

of male and di�erently aged employees are included as controls. Our results

show that �rms with more diverse workforce in terms of skills/education are

more likely to patent in more than one technological �eld. Finally, we �nd that

the bene�cial e�ect of skill and ethnic diversity on innovation materializes for

the white-collar occupations only, and not for the blue-collar occupations. These

results support the hypothesis that diverse workers tend to have a wider pool

of di�erent experiences, knowledge bases and heuristics boosting their problem-

solving capacities and creativity, which in turn facilitate innovations. In this

5

regard, our �ndings are consistent with the theoretical frameworks proposed by

Hong and Page (2001 and 2004) and Berliant and Fujita (2008). Hence, our re-

sults suggest �rms to focus on recruitment strategies that explicitly account for

heterogeneity in skills and ethnicity. This article may also provide some sugges-

tions to public authorities in terms of innovation policies. Given that innovation

is considered as one of the most important components for the long-term eco-

nomic growth (new growth theory), hence investigating the determinants of the

innovation process may also lead to the identi�cation of the sources of a sustain-

able growth. In this regard, public institutions and policy makers could invest

resources to promote skill and ethnic diversity within workplaces and in such a

way increase the innovation, and ultimately the economic growth.

The structure of the paper is as follows: section 2 brie�y describes the data,

section 3 provides details on the empirical strategy, sections 4 and 5 explain

all the results of our empirical analyses and section 6 o�ers some concluding

remarks.

2 Data

2.1 Data sources

The data set we use for our analysis is obtained by merging three di�erent

data sources from Denmark. The �rst one is the `Integrated Database for Labor

Market Research' (IDA), which is a register-based LEED managed by Statistics

Denmark, a Danish governmental institute responsible for creating statistics

on the Danish society and economy. IDA contains a broad set of information

on individuals and �rms for years 1980-2006. In particular, we are interested

in gender, age, nationality, education, occupation and place of work, but also

whether a �rm is (partially or totally) foreign-owned and a multi-establishment.

6

The second data source is a register of �rms' business accounts (REGNSKAB)

that provides information on a number of �nancial items, which we need in order

to construct values of �rms' capital stock, information on whether a �rm is an

exporter and the 3-digit industry, in which the �rm operates. This database is

also maintained by the Statistics Denmark and reports data for the period 1995-

2006.3 In REGNSKAB it is possible to identify partially and totally imputed

values, which we exclude from our �nal data set in order to avoid any bias in

the estimates. The last data source is a collection of patent applications sent

to the European Patent O�ce (EPO) by Danish �rms.4 It covers a period of

26 years (1978-2003) and allows us to account for 2822 applicants and 2244

granted �rms.5 We disregard those industries where there were no patenting

�rms during the period covered in our empirical analysis.6 We also exclude

enterprises with less than 10 employees from our sample to allow all investigated

�rms potentially to reach the highest degree of (ethnic) diversity at least when

an aggregated speci�cation is used. Thus, our �nal data set contains information

on approximately 14,000 �rms per year over a period of 9 years (1995-2003).

2.2 Diversity measures

The workforce diversity (heterogeneity) measures used in this article are

3Part of the statistics in REGNSKAB refers to selected �rms for direct surveying: all �rmswith more than 50 employees or pro�ts higher than a given threshold. The rest is recordedin accordance with a strati�ed sample strategy. The surveyed �rms can choose whether tosubmit their annual accounts and other speci�cations or to �ll out a questionnaire. In orderto facilitate responding, questions are formulated in the same way as required in the Danishannual accounts legislation.

4The access to these data has been made possible thanks to the Center for Economic andBusiness Research (CEBR), an independent research center a�liated with the CopenhagenBusiness School (CBS).

5More details concerning the construction and composition of the data set can be found inKaiser, Kongsted and Rønde (2008).

6

Speci�cally, we drop the following industries: agriculture, �shing and quarrying; electricity,gas and water supply; sale and repair of motor vehicles; hotels and restaurants; transports;and public services.

7

computed at the �rm level and based on the Her�ndahl index. The latter

combines two important dimensions of diversity: the �richness�, which refers

to the number of de�ned categories within a �rm, and the �evenness�, which

informs on how equally populated such categories are. Speci�cally, our diversity

measures represent weighted averages of Her�ndahl indexes computed at the

workplace level:

Div_hit =

W∑w=1

NwNi

(1−

S∑s=1

p2wst

),

where Div_hit is the diversity index of �rm i at time t for the dimension h, W

is the total number of workplaces (w refers to a given workplace) constituting

the �rm, and therefore Nw and Ni denote the total number of workers at the

workplace and �rm levels, respectively. Thus, the ratio between the last two

variables corresponds to the weighting function, while pwst is the proportion

of the workplace's employees falling into each category s at time t, with s =

1, 2, ..., S. The diversity index has a minimum value, which takes value on zero

if there is only one category represented within the workplace, and a maximum

value equal to(1− 1

S

)if all categories are equally represented. The index can

be interpreted as the probability that two randomly drawn individuals in a

workplace belong to di�erent groups.

As we distinguish between cultural, educational (skill) and demographic di-

versity, a separate measure is computed along each of the three cited dimensions.

Diversity in cultural background is associated with employees' country of origin7

and is built by using the following eight categories: North America and Oceania,

Central and South America, Africa, Western and Southern Europe, Formerly

Communist Countries, East Asia, Other Asia, Muslim Countries.8 Diversity in

education is based on six categories. In particular, tertiary education (PhD,

Master and Bachelor) is divided into the following four groups: engineering,

7Native Danes and the second generation of immigrants are excluded.8See Appendix1 for more details about the countries belonging to each ethnic category.

8

humanities, natural sciences and social sciences. The other two categories are

represented by secondary and compulsory education. Eight categories instead

refer to the demographic diversity, which is computed by combining gender

and four age dichotomous indicators associated with quartiles of the overall age

distribution.

However, given that the overall categorization might be somehow arbitrary,

we decide to use a more disaggregated one, too. The alternative cultural back-

ground diversity is based on linguistic classi�cation.9 Speci�cally, we group

foreign employees together by family of languages, to which the language spo-

ken in their home country belongs. Using the third linguistic tree level language

classi�cation drawn from Ethnologue, we end up having 40 linguistic groups.10

Further, our disaggregated diversity indexes in education and demographics are

based on eight and ten categories, respectively. Di�erently from the former

classi�cation, the secondary education is split into 3 sub-groups: high school,

business high school and vocational education. Demographic diversity is com-

puted by combining gender and �ve age dichotomous indicators associated with

quintiles of the overall age distribution.

2.3 Descriptive statistics

Table 1 reports some descriptive statistics (median, mean and standard de-

viation) of the variables used in our empirical analysis. The �rm population

is divided into two groups based on whether a �rm applied for at least one

patent (patenting �rm) or did not. Patenting �rms are characterized by notably

9Previous literature argues that linguistic distance serves as a good proxy for culturaldistance (Guiso, Sapienza and Zingales, 2009; Adsera and Pytlikova, 2010).

10The linguistic classi�cation is more detailed than the grouping by nationality. Speci�-cally, we group countries (their major o�cial language spoken by the majority in a particularcountry) by the third linguistic tree level, e.g. Germanic West vs. Germanic North vs. Ro-mance languages. The information on languages is drawn from the encyclopedia of languages�Ethnologue: Languages of the World�, see the Appendix section for more details about thelist of countries and the linguistic groups included.

9

higher values of capital and labor inputs: the average capital stock is almost

9.5 times the value of the non-patenting �rms. The latter are more likely to be

single-establishment companies and markedly less export-oriented: on average

the share of exporters halves among those �rms that have never applied for a

patent. Small di�erences are shown instead for the foreign ownership status: the

foreign capital penetration is quite low among Danish �rms. For the purposes of

our analysis it appears relevant to take into account the role of external sources

of knowledge since they may facilitate �rms' innovation activity. Although we

already control (using the export dummy) whether �rms compete in the inter-

national arena and then have access to foreign knowledge, more precise indexes

of knowledge spillovers can be de�ned at the national level. Speci�cally, we

construct two measures of knowledge spillovers, one based on the geographical

distance and the other on the technological proximity, see Appendix 2 for a de-

tailed description of the external knowledge indexes. Looking at these measures

of knowledge spillovers, see Table 1, we �nd no evidence of di�used clustering

behavior or huge di�erences in technological distance between the two groups

of �rms.

As evident from the Table 1, there are remarkable di�erences between patent-

ing and non-patenting �rms with respect to �rms' workforce composition. Not

surprisingly, patenting �rms are characterized by larger shares of highly edu-

cated employees, white-collar workers and managers, whereas the opposite holds

true for middle managers. Interestingly, patenting �rms also record a higher

share of female and foreign employees. Workers in these knowledge-based �rms

are slightly older on average terms: presumably the share of young employees

is lower because patenting �rms hire a wider proportion of well trained and

experienced people. As a matter of fact long tenure pro�les are more common

within patenting �rms' environment. Diversity indexes register higher values

10

for patenting �rms. Particularly evident is the di�erential in the ethnic hetero-

geneity that is 3.5 times larger on average with respect to non-patenting �rms.

These indexes also report substantial lower skill diversity, which is 16% poorer

in mean values. Thus, the presented descriptives raise a reasonable interest

in evaluating the �nexus� between �rms' patenting behavior and diversity in

ethnicity, education and demographics.

3 Econometric methods

3.1 Propensity to innovate

To investigate the e�ect of labor diversity on �rm's propensity to innovate,

we employ a standard binomial regression technique in our analyses. Speci�cally,

we estimate the following probit model:

zit = 1 if z∗it > 0

zit = 0 otherwise

with z∗it = γcDiv_cit + γsDiv_sit + γdDiv_dit + x′

itβ + ηi + vit

where z∗it denotes the unobservable variable inducing �rm i to apply at least

once for a patent at time t; zit indicates whether �rm i concretely has applied at

time t; the �rst three terms at the right-hand side are diversity in cultural back-

ground, skills and demographics respectively. The vector x′

it includes an exten-

sive set of observable characteristics, like, among others, the external knowledge

indexes and the �rm-speci�c characteristics described in section 2.3; ηi denotes

the �rm-speci�c unobservable e�ect and vit is the error term. Similar to Blun-

dell, Gri�th and Van Reenen (2002), we proxy for the unobserved heterogeneity

11

ηi by arguing that the main source of unobserved permanent di�erences in �rms'

capabilities to innovate can be captured by the pre-sample history of innova-

tive successes. In line with that, we assume that the �rms' average number

of patent applications provides a good approximation of the above unobserv-

able heterogeneity component ηi. However, an overall increase in the number

of patent applications is recorded during the pre-sample period. Thus, as in

Kaiser, Kongsted and Rønde (2008) we deal with that by normalizing a �rm's

number of patents in a pre- sample year by the total number of patents applied

for during that year:

ηi =1

T

T+τ∑t=τ

(yit∑Ii=1 yit

)

As �rms can leverage labor diversity to improve their innovation perfor-

mances, we also instrument our variables of interest in order to obtain a causal

e�ect of workforce diversity on �rm innovation activities. Speci�cally, we imple-

ment an instrumental variable (IV) strategy based on the levels of workforce di-

versity in cultural background, skills and demographic characteristics computed

at the commuting area11 where the �rm is located.12 The so-called functional

economic regions or commuting areas are identi�ed using a speci�c algorithm

based on the following two criteria. Firstly, a group of municipalities consti-

tute a commuting area if the interaction within the group of municipalities is

high compared to the interaction with other areas. Furthermore, at least one

municipality in the area must be a center, i.e. a certain share of the employees

living in the municipality must work in the municipality, too (Andersen, 2000).

11The commuting area level diversity is calculated by averaging the �rm level diversity ofall �rms belonging to a speci�c commuting area.

12Unfortunately in our data set it is not possible to observe in which area each establishmentof a multi-establishment �rm is located. For the multi-establishments �rms, the informationabout the location is only provided for the headquarter. However, we do not think thisrepresents a serious problem as multi-establishments �rms constitute only 26 % of our sample.This is reinforced by the fact that we always reject the hypothesis that our instrument is weak.

12

In total there are 51 commuting areas identi�ed, see Figure 1.

We believe that diversity at the commuting area level presents a suitable

supply driven instrument for workplace level diversity because commuting ar-

eas in Denmark (except for the area around Copenhagen) are typically relatively

small and therefore �rms very likely recruit workers from a given local supply

of labor, which is characterized by a certain degree of heterogeneity. This ar-

gument is further reinforced by the role of networks in the employment process

(Montgomery, 1991, Munshi, 2003). Thus �rms placed in areas with a high

labor diversity are also more likely to employ more a diverse workforce. More-

over, the rather low residential mobility in Denmark (Deding, Filges and Van

Ommeren, 2009) seems to support the properness of our IV strategy.13 To re-

inforce the exogeneity of our instruments we exclude each �rm workforce from

the computation of labor diversity at the related commuting area. We use the

same IV strategy for the analyses of intensive and extensive margins.

3.2 Intensive margins

As the number of patents is by de�nition restricted to non-negative integers,

the econometric strategy used to analyze the relationship between intensive

margins of patenting activity and labor diversity is grounded on the family of

count models. As a starting point we assume that the data generating process

follows a Poisson distribution. If the random variable Yit, in our case number

of patent applications �led by �rm i at time t, is Poisson distributed, then the

probability that exactly y applications are observed is as follows

13Furthermore, one may point towards potentially endogenous location behavior of immi-grants. The validity of our instrument may be strengthened by the spatial dispersion policyimplemented for immigrants between 1986 and 1998 by the Danish authorities. The dispersalpolicy implied that new refugees were randomly distributed across locations in Denmark, seee.g. Damm (2009).

13

P (Yit = y | λit) =e−λitλy

y!.

Covariates can be introduced by specifying the individual mean as

λit = exp(βcDiv_cit + βsDiv_sit + βdDiv_dit + w

′

itβw + ηi

), (1)

where ηi stands for the unobserved time-invariant �rm-speci�c heterogeneity

term and wit is a vector of patent production determinants, as speci�ed in

subsection 3.1. Following Blundell, Gri�th and Van Reenen (1995), we also

include, among the covariates wit, the discounted patent stock of �rm i at

period t − 1 in order to account for potential state dependence in patenting

activity. This is calculated as

disc_stockit−1 = yit−1 + (1− δ)disc_stockit−2 ,

where yit−1 is the lagged number of patent applications and δ is the depreciation

rate set equal to 30 per cent as in Blundell, Gri�th and Van Reenen (1995).

We also add a dummy variable taking value on zero if the �rm had never

innovated prior to 1995, to capture persistent di�erences between patenting

and non-patenting �rms (Blundell, Gri�th and Van Reenen, 1995; Blundell,

Gri�th and Van Reenen, 1999). In addition, this dummy variable represents a

remedy for the so-called "zero-in�ation problem" given that in our data many

�rms never applied for a single patent. The pre-sample information technique

is feasible in a study like ours because we have a long series for the dependent

variable (1977-1994) prior to the starting period (1995) of the �nal sample in

14

use.

However, as the Poisson model imposes the equality of conditional mean

and conditional variance of the dependent variable distribution, we also decide

to implement a negative binomial model, which is more �exible. In fact, it

allows the variance to exceed the mean and the dispersion parameter α to vary

randomly between �rms14:

P (Yit = y | αi, λit) =Γ(y + α−1

)y!Γ (α−1)

(α−1

α−1 + λit

)α−1 (λit

α−1 + λit

)y,

where Γ is the Gamma distribution.

As we have mentioned before, one may argue that the relationship between

�rm-patenting activity and diversity could be a�ected by endogeneity. The

latter issue might arise because there could be unobserved �rm-speci�c factors

in�uencing both the number of patent applications and the degree of labor

diversity. To address these concerns, we apply a two-stage IV procedure to

the Poisson model as suggested by Vuong (1984). In this case, equation (1) is

speci�ed as follows:

λit = exp(βcDiv_cit + βsDiv_sit + βdDiv_dit + w

′

itβw + ηi + uit

)(2)

where the term uit can be interpreted as unobserved heterogeneity correlated

with the diversity indexes but uncorrelated with the vector of patent production

determinants wit.15 To model the correlation between the endogenous variables

and uit, we specify a system of linear reduced-form equations, one for each

diversity index. This is

14The Negative binomial model coincides to a Poisson distribution when α = 0 .15The error term uit is added to allow for endogeneity. It also induces overdispersion, so

that the Poisson model and the Negative binomial model are empirically equivalent.

15

Div_cit = w

′

itγw + z′

itγz + εcit

Div_sit = witγw + z′

itγz + εsit

Div_dit = w′

itγw + z′

itγz + εdit

where zit is the vector of exogenous variables that a�ects �rm level diversity,

but does not directly a�ect the number of patent applications. As in section

3.1, the excluded variables are the diversity indexes computed at the commuting

area where the �rm is located and the model is just-identi�ed. The error terms ε

are assumed to have zero mean and to be correlated across equations for a given

�rm i, but uncorrelated across observations. Furthermore, we assume that the

errors u and ε are related via

uit = ρcεcit + ρsεsit + ρdεdit + ζit (3)

where ζit ∼[0, σ2

ζ

]is independent of εcit, εsit and εcit.

16 Substituting equation

(3) in equation (2) for uit and taking the expectation with respect to ζ yields

Eζ(λ) = exp(βcDiv_c+βsDiv_s+βdDiv_d+w′β+η+lnE(eζ)+ρcεc+ρsεs+ρdεd) .

The constant term lnE(eζ) can be absorbed in the coe�cient of the intercept

as an element of w. It follows that

λit = exp(βcDiv_cit + βsDiv_sit + βdDiv_dit + w

′itβw + ηi + ρcεcit + ρsεsit + ρdεdit

),

where εcit, εsit and εcit are the new additional variables. Given that the former

variables are unobservable, we follow a two-step estimation procedure where

we �rst estimate and generate them and second we estimate parameters of

the Poisson model after replacing εcit, εsit and εcit with ε̂cit, ε̂sit and ε̂cit .

Obviously, the variance and covariance matrix of the two-step estimator needs

16This assumption means that ε is a common latent factor that a�ects both diversity andpatent applications and is the only source of dependence between them, after controlling forthe in�uence of the observable variables.

16

to be adjusted for the above replacement by bootstrapping the sequential two-

step estimator.

3.3 Extensive margins

The estimation approach used to evaluate the extensive margins of �rms'

patenting behavior is similar to the one adopted for the �rms' propensity to

patent. Although the count data models would be more suitable for the anal-

yses of relationship between workforce diversity and the number of di�erent

technological areas of patent application, our data and concretely the lack of

minimum observations required to run count data models do not allow us to use

them. Instead, we evaluate whether more labor diversity increases the proba-

bility of a �rm to (apply for a) patent in more than one technological area.

4 Results

This section reports �ndings for each of the outcome dimensions we look at:

propensity to innovate, intensive and extensive margins. Several speci�cations

among the di�erent econometric models here employed help in understanding

the strength of our results. Further, in the sensitivity analyses subsection we

examine whether the results di�er between white- and blue-collar occupations,

and across alternative diversity and innovation measures.

4.1 Results on labor diversity and propensity to innovate

Table 2 reports estimates concerning the propensity to patent. As explained

in the previous section, we implement probit models having as dependent vari-

able the dummy indicating whether a �rm has applied for a patent in a given

17

year. In column 1 we show a model with the three workforce diversity indexes

as the only regressors. The workforce diversity can explain about 15% of the

overall variation in the dependent variable and is associated with sizable and

signi�cantly positive e�ects. Augmenting the speci�cation by including indus-

trial, time and size dummies reduces the size of our coe�cients of interest and

almost doubles the explanatory power of the model. Columns 3 and 4 show

results from probit models with all other covariates; while the former treats the

diversity indexes as exogenous variables, the latter shows the IV speci�cation

with workforce diversity levels at commuting areas as instruments for the �rm

workforce diversity. Results between the two full speci�cation models are rather

similar and imply that a standard deviation change in the ethnic and skill di-

versity increases the probability to apply for patent by 0.020 and 0.044 percent,

respectively.

Turning to the other control variables, the inclusion of pre-sample �xed

e�ects turns out to be important to deal with time invariant unobserved het-

erogeneity among �rms. The variable attaches statistically signi�cant positive

coe�cient and it also corrects the estimates on labor diversity. Further, �rms

with higher shares of highly skilled and vocational workers, and exporting �rms

have higher propensity to patent. Instead, the knowledge spillovers based on

technological and geographical distances, and the average �rm tenure do not

explain much of such a propensity.

As explained above, we run additionally the models using diversity indexes

based on more detailed category speci�cation; the results are shown in the Table

2, columns 5 to 8. Now the e�ect of a standard deviation change in the skill

diversity produces an increase in the probability to apply for a patent by 0.059

percent, whereas the positive e�ect of ethnic diversity appears negligible.

18

4.2 Results on labor diversity and intensive margins

In the next step, we analyze how �rm workforce diversity contribute to the

number of patent application. Tables 3 and 4 show the results of the intensive

margins analyses using the aggregate speci�cation of diversity in cultural back-

ground based on nationality and using the disaggregated speci�cation based on

linguistic grouping classi�cation, respectively. The estimated coe�cients rep-

resent elasticities. The �rst column in Table 3 shows the output of a Poisson

regression having only the diversity measures as regressors: the coe�cients to

all diversity indexes are large, positive and signi�cant. Once more, after in-

cluding the industry, time and size dummies (column 2) and especially in the

full model speci�cation (column 3) their dimension and statistical signi�cance

decreases. Nonetheless, except for the demographic heterogeneity, the diversity

indexes retain their statistically signi�cant positive coe�cients; also if we apply

the instrumental estimation strategy (column 4). Taking the last IV Poisson

speci�cation as the most reliable, we �nd that one percent increase in the skill

diversity leads to 1.7 percentage increase in the number of patent applications.

This e�ect is quite sizable given that the elasticity associated to a production

input like human capital (proxied by the share of highly skilled workers) is just

about 1.6 times larger. Important e�ects are also related to the shares of techni-

cians, capital and labor stock, while knowledge spillovers variables do not show

signi�cant contributions to the overall number of patent applications. As in

the case of patenting propensity, exporters bene�t from the knowledge gained

in the international markets. The results obtained by implementing the more

�exible negative binomial models are, except for the e�ect of ethnic diversity,

very similar to the results from the Poisson models, see Table 3, columns 5 to

7.

Table 4 reports results for the disaggregated classi�cation of labor diversity

19

dimensions. Although the results are very similar to the results using aggre-

gate diversity speci�cation shown in Table 3, there are some changes worth

noting. Speci�cally, in the IV Poisson (column 4) the coe�cient on the ethnic

diversity turns to be insigni�cant; in addition a larger e�ect is associated with

heterogeneity in skills. According to this speci�cation a one percent increase in

the educational heterogeneity implies a 2.23 percent increase in the number of

patent applications.17

4.3 Results on labor diversity and extensive margins

Table 5 reports the e�ects of labor diversity on the probability of applying

for a patent in di�erent technological areas in a given year. The structure of this

table is similar to Table 2. The low number of annual patent applications in each

technological area does not allow us to use potentially more suited count mod-

els. Regarding the variables of interest, we �nd that the diversity indexes alone

explain 6.8 percent of the overall variation in the dependent variable, coe�cients

to all diversity indexes are positive and statistically signi�cant. However, the

signi�cance of the diversity in cultural background and demographics vanishes

when all covariates are included. Overall, we �nd that skill diversity is impor-

tant for patenting in di�erent technological areas. Taking the lowest estimate

between the full IV speci�cations, it turns out that a standard deviation increase

in skill diversity is associated to a raise of about 7.2 percent in the probability

to patent in di�erent technological �elds. Thus it seems as the skill diversity is

17

We have also investigated whether the e�ects of a particular dimension of diversity can bein�uenced by other forms of labor heterogeneity by inclusion of all possible interaction cou-ples between the diversity indexes. Furthermore, driven by the hypothesis that there mightbe complementarities among di�erent skills and demographic groups, in particular young andeducated workers together with a more diverse workforce can stimulate innovation and cre-ativity, we have augmented our models with interactions between diversity indexes and sharesof highly skilled and younger workers. Nevertheless, none of the interactions turned out tobe statistically signi�cant. Figures showing marginal e�ects of the interactions are availablefrom the authors upon request.

20

much more relevant for patenting in di�erent technological areas than for the

patenting per se.

5 Sensitivity analysis

In the next section, we examine whether the e�ects of labor diversity on

patenting activity of �rms di�er between di�erent occupational categories of

workers, and across alternative diversity and innovation measures.

Firstly, we calculate our diversity indexes for white- and blue-collar occupa-

tions and include both of them in the same regression. This is driven by the

idea that diversity could play a di�erent role for distinct occupational groups

and consequently have diverse e�ects on �rm innovation. In particular, we

would expect that the bene�cial e�ects of diverse problem-solving abilities and

creativity would materialize more in terms of innovation for white-collar occupa-

tions compared to blue-collar occupations. On the other hand, the white-collar

occupations are more likely to rely on sound communication between workers

compared to blue-collar occupations. Thus, we would expect large positive ef-

fects of skill/educational diversity on innovation for white collar occupations,

whereas the e�ects of demographic and cultural diversity can be ambiguous de-

pending on which e�ect prevails: the positive e�ect from better decision making,

problem-solving abilities and creativity, or negative e�ect caused by the com-

munication costs and costs of �cross-cultural dealing�. The results of the e�ect

of diversity indexes calculated separately for the two occupational groups on

�rm probability to innovate and number of patent applications are presented in

Table 6 (columns 1 and 2) and Table 7 (columns 1 and 2), respectively. Indeed,

we �nd that workforce diversity is much more important for white-collar than

for blue-collar occupations. The e�ect of skill and ethnic diversity among the

white-collar workers on probability to innovate and the number of patents is

21

positive and statistically signi�cant. On contrary, the e�ect of demographic di-

versity is insigni�cant for both white- and blue-collar occupations. In the model

using detailed/disaggregated diversity indexes, the e�ect of demographic diver-

sity on the number of patent applications turns out to be negative, see Table

7. This is partly consistent with the notion by Lazear (1999). Finally, it is also

worth noting that the positive e�ect of ethnic and skill diversity is larger for the

number of patent application than for the probability to innovate.

Further, as a part of the sensitivity analysis we evaluate eventual varia-

tions in the e�ects of labor diversity when the diversity measure is di�erently

computed. In particular, we use two alternative diversity indexes: the Shannon-

Weaver entropy and the richness indexes. The former is considered as one of

the most profound and useful diversity indexes in biology (Maignan, Ottaviano,

Pinelli and Rullani, 2003), whereas the latter is de�ned as a number of categories

observed for each dimension of interest (it does not account for the �evenness�

dimension). Final checks come from the evaluation of the relationship between

labor diversity and �rms' granted patents rather than from patent applications.

The reason behind this sensitivity is based on the potential critique that appli-

cations may not result in granted patents18. Table 6 reports marginal e�ects

of the three dimensions of labor diversity on the �rm probability to innovate.

These �ndings do not substantially di�er from the main results. Interestingly,

the role of skill heterogeneity strengthens when the outcome variable is based on

patent grants rather than applications. This result is also con�rmed in analysis

of the number of patents shown in Table 7. Overall, the results of sensitivity

analysis are in line with the main �ndings and hence their overall interpretation

18Unfortunately, given the relatively low number of patenting �rms (and the delay occurringbetween the application year and period in which the potential grant is received), it is notpossible to evaluate how our main �ndings might have changed for the probability to patentin di�erent technological areas in a given year. The results with the alternative diversityindexes, Shannon-Weaver entropy and the richness indexes, are available from the authorsupon request.

22

does not vary. That notably corroborates our main analyses and provides an

evident support to the conclusions, which are summarized in the next section.

6 Discussion and conclusions

In this paper we provide an overall assessment of the nexus between labor

diversity and �rms' patenting behavior. To the best of our knowledge, this study

represents the �rst concrete attempt to formalize and generalize the relation-

ship of labor diversity and innovation by using detailed information on �rms'

workforce composition.

Speci�cally, controlling for a large number of �rm-speci�c characteristics,

proxying for time-invariant unobservables, including reasonable measures of

knowledge spillovers, adopting alternative categorizations for diversity and using

proper instruments for the labor diversity dimensions of interest, we �nd robust

evidence that diversity in education and skills of the labor force is a fundamental

source of innovation. That facilitates �rms' patenting activity in several ways:

(a) slightly increases their propensity to (apply for a) patent, (b) increases the

overall number of patent applications and (c) enlarges the breadth of patenting

technological �elds. Being prudent in the quanti�cation of skill heterogeneity

e�ects on all these aspects of patenting activities, we �nd that a percentage

change in skill diversity increases the number of �rms' patent applications by

1.7 percent. The contribution of skill diversity in terms of general propensity

to send at least one patent application in a given year is quite modest: a stan-

dard deviation change in its value turns to raise such a probability by 0.044

percent. However, the e�ect of educational diversity on extensive margins is

large, a standard deviation change in skill diversity leads to a raise of about 7.2

percent in the �rms' probability to apply for a patent in di�erent technological

areas. Thus, in order to widen the patent technological spectrum it seems to

23

be fundamental to increase the heterogeneity in the workers' competencies and

knowledge bases. Furthermore, we �nd a statistically signi�cant positive e�ect

of ethnically diverse workforce on the propensity to innovate and on the number

of patent applications. The valuable in�uence of ethnic diversity, albeit smaller

in its magnitude than the e�ect coming from skill/educational labor diversity, is

not negligible. Regarding the results of demographic diversity on innovation, its

e�ect typically vanishes once detailed �rm-speci�c characteristics are included

as control variables. Finally, we �nd that the bene�cial e�ect of skill and ethnic

diversity on innovation materializes for the white-collar occupations only, and

not for the blue-collar occupations. These results support the hypothesis that

diverse workers tend to have a wider pool of di�erent experiences, knowledge

bases and heuristics boosting their problem-solving capacities and creativity,

which in turn facilitate innovations. In this regard, our �ndings are consistent

with the theoretical frameworks proposed by Hong and Page (2001 and 2004)

and Berliant and Fujita (2008).

The overall picture coming out from our empirical analysis seems to be par-

ticularly relevant not only for the design of �rms' innovation strategies but also

for public policies aimed at fostering innovation. Our results give an important

insight into the technological process, a driver of productivity growth and ul-

timately an economic growth. We �nd that an increase in �rm labor diversity

in terms of education and ethnicity has a positive e�ect on the �rm innovation

process. Thus, governmental policies aimed to encourage the employment of

workers with di�erent educational and cultural backgrounds can be bene�cial

in terms of improvements in �rms' patenting activities, increasing both private

returns, directly, and social gains, through knowledge di�usion mechanisms.

Nowadays, such policies might contribute to attract foreign and domestically

less abundant skilled labor by supporting investments in human capital. That

24

could be one of the determinants to invert the general decline in patenting ac-

tivity recorded during the recent economic crisis among the OECD countries

(OECD, 2009).

25

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29

Appendix 1: Measurement of ethnic diversity

1) The citizens in the di�erent nationality groups are: Danish, Danish native in-

cluding second generation immigrants; North America and Oceania, United

States, Canada, Australia, New Zealand; Central and South America, Guatemala,

Belize, Costa Rica, Honduras, Panama, El Salvador, Nicaragua, Venezuela,

Ecuador, Peru, Bolivia, Chile, Argentina, Brazil; Formerly Communist Coun-

tries, Armenia, Belarus, Estonia, Georgia, Latvia, Lithuania, Moldova, Russia,

Tajikistan, Ukraine, Bulgaria, Czech Republic, Hungary, Poland, Romania, Slo-

vakia, Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Rep. of Macedonia,

Montenegro, Serbia, and Slovenia; Muslim Countries, Afghanistan, Algeria,

Arab Emirates, Azerbaijan, Bahrain, Bangladesh, Brunei Darussalem, Burk-

ina Faso, Camoros, Chad, Djibouti, Egypt, Eritrea, Gambia, Guinea, Indone-

sia, Iran, Iraq, Jordan, Kazakstan, Kirgizstan, Kuwait, Lebanon, Libyan Arab

Jamahiriya, Malaysia, Maldives, Mali, Mauritania, Morocco, Nigeria, Oman,

Pakistan, Palestine, Qatar, Saudi Arabia, Senegal, Sierra Leone, Somalia, Su-

dan, Syria, Tadzhikstan, Tunisia, Turkey, Turkmenistan, Uzbekistan, Yemen;

East Asia, China, Hong Kong, Japan, Korea, Korea Dem. People's Rep. Of,

Macao, Mongolia, Taiwan; Asia, all the other Asian countries non included

in both East Asia and Muslim Countries categories and Africa, all the other

African countries not included in the Muslim Country; Western and South-

ern Europe, all the other European countries not included in the Formerly

Communist Countries category.

2) Using linguistic grouping: Germanic West (Antigua Barbuda, Aruba, Aus-

tralia, Austria, Bahamas, Barbados, Belgium, Belize, Bermuda, Botswana, Brunei,

Cameroon, Canada, Cook Islands, Dominica, Eritrea, Gambia, Germany, Ghana,

Grenada, Guyana, Haiti, Ireland, Jamaica, Liberia, Liechtenstein, Luxemburg,

Mauritius, Namibia, Netherlands, Netherlands Antilles, New Zealand, Saint

Kitts and Nevis, Saint Lucia, Saint Vincent and Grenadines, Seychelles, Sierra

Leone, Solomon Islands, South Africa, St. Helena, Suriname, Switzerland,

Trinidad and Tobago, Uganda, United Kingdom, United States, Zambia, Zim-

babwe), Germanic Nord (Denmark, Iceland, Norway, Sweden), Slavic West

(Czech Republic, Poland, Slovakia), Slavic South (Bosnia and Herzegovina,

Croatia, Serbia, Slovenia), Slavic East (Belarus, Georgia, Mongolia, Rus-

sian Federation, Ukraine), Baltic East (Latvia, Lithuania), Finno-Permic

30

(Finland, Estonia), Ugric (Hungary), Romance (Andorra, Angola, Argentina,

Benin, Bolivia, Brazil, Burkina Faso, Cape Verde, Chile, Columbia, Costa Rica,

Cote D'Ivoire, Cuba, Djibouti, Dominican Republic, Ecuador, El Salvador,

Equatorial Guinea, France, French Guina, Gabon, Guadeloupe, Guatemala,

Guinea, Guinea Bissau, Holy See, Honduras, Italy, Macau, Martinique, Mexico,

Moldova, Mozambique, Nicaragua, Panama, Peru, Portugal, Puerto Rico, Re-

union, Romania, San Marino, Sao Tome, Senegal, Spain, Uruguay, Venezuela),

Attic (Cyprus, Greece), Turkic South (Azerbaijan, Turkey, Turkmenistan),

Turkic West (Kazakhstan, Kyrgystan), Turkic East (Uzbekistan), Gheg

(Albania, Kosovo, Republic of Macedonia, Montenegro), Semitic Central (Al-

geria, Bahrain, Comoros, Chad, Egypt, Irak, Israel, Jordan, Kuwait, Lebanon,

Lybian Arab Jamahiria, Malta, Mauritiania, Morocco, Oman, Qatar, Saudi Ara-

bia, Sudan, Syrian Arab Republic, Tunisia, Yemen, United Arabs Emirates),

Indo-Aryan (Bangladesh, Fiji, India, Maldives, Nepal, Pakistan, Sri Lanka),

Mon-Khmer East (Cambodia), Semitic South (Ethiopia), Malayo-Polynesian

West (Indonesia, Philippines), Malayo-Polynesian Central East (Kiribati,

Marshall Islands, Nauru, Samoa, Tonga), Iranian (Afghanistan, Iran, Tajik-

istan), Betai (Laos, Thailand), Malayic (Malasya), Cushitic East (Somalia),

Viet-Muong (Vietnam), Volta-Congo (Burundi, Congo, Kenya, Lesotho,

Malawi, Nigeria, Rwanda, Swaziland, Tanzania, Togo), Barito (Madagascar),

MandeWest (Mali), Lolo-Burmese (Burma), Chadic West (Niger), Guarani

(Paraguay), Himalayish (Buthan), Armenian (Armenia), Sino Tibetan

(China, Hong Kong, Singapore, Taiwan), Japonic (Japan, Republic of Korea,

Korea D.P.R.O.).

Appendix 2: External knowledge indexes

The main literature on agglomeration economies emphasizes the importance of

�rm's local environment, which may re�ect information advantages, labor or

other inputs pooling and further bene�cial network e�ects aimed at alleviating

the burden represented by �xed costs. A seminal contribution in this �eld is

due to Audretsch and Feldman (1996), who �nd that industries characterized

31

by elevated R&D intensity or particularly skilled labor forces present a greater

degree of geographic concentration of production. Other relevant studies like

Wallsten (2001) and Adams and Ja�e (1996) provide evidence of the geographic

extent of knowledge spillovers by computing the distance in miles between each

�rm-pair. However, the geography is not the only dimension of the external

knowledge. In fact, there exists at least another approach which focuses on the

concept of technological proximity (Ja�e, 1986; Adams, 1990). Speci�cally, the

idea that the technology developed by a �rm can a�ect other �rms, even though

they are not geographically close or no transactions of goods occur between

them, has led to the de�nition of technological proximity as closeness between

�rm-pairs' technological pro�les.

Following both the cited approaches, we construct two indexes of knowledge

spillovers. These are weighted sums of �rms' codi�ed knowledge proxied by the

discounted stock of patent applications.19 The weighting function for the �rst

index refers to the geographical distance between pairs of workplaces' munici-

palities and is computed by using the �rms' latitude and longitude coordinates

(the address of their headquarters). Speci�cally, assuming a spherical earth of

actual earth volume, this method allows us to measure the distance in kilome-

ters between any pair of �rms i and j .20 The �rst knowledge spillover index is

then computed as follows:

K_geoit =1

edistij

I∑j 6=i

disc_stockjt .

The second index is instead based on the technological proximity. Following

Adams (1990), we use the shares of di�erently skilled workers to de�ne our

19See paragraph 4.2.20We use the following formula dij = 6378.7 ∗ acos{sin(lati/57.2958) ∗ sin(latj/57.2958)+

+cos(lati/57.2958) ∗ cos(latj/57.2958) ∗ cos(lonj/57.2958− loni/57.2958)} .

32

alternative weighting function ψij that is the uncentered correlation:

ψij =fif

′

j[(fif

′i

) (fjf

′j

)]1/2 .

The components of the generator vector f re�ects �rm's workforce compo-

sition in terms of skills using the disaggregated categorization as described in

section 3.2. The second measure of knowledge spillover pool is therefore de�ned

as

K_techit = ψij

I∑j 6=i

disc_stockjt .

Thus, both K_geoit and K_techij contain weighting functions that might

capture the so-called �rm's absorptive capacity, which is the ability to identify

and exploit the knowledge externally produced (Cohen and Levinthal, 1990).

33

Tab

le1:

Des

crip

tive

stat

isti

cs

All

sam

ple

Non

-pat

enti

ng

firm

sP

aten

ting

firm

sV

aria

ble

sD

efinit

ion

Med

ian

Mea

nSd

Med

ian

Mea

nSd

Med

ian

Mea

nSd

IDA

Var

iable

s:m

ales

men

asa

pro

por

tion

ofal

lem

plo

yees

0.78

60.

709

0.24

30.

786

0.70

60.

247

0.17

40.

674

0.19

9fo

reig

ner

snon

-dan

ish

emplo

yees

asa

pro

por

tion

ofal

lem

plo

yees

00.

042

0.08

60

0.42

30.

494

10.

750

0.43

3ag

e1em

plo

yees

aged

15-2

8as

apro

por

tion

ofal

lem

plo

yees

0.30

40.

325

0.17

20.

304

0.32

50.

173

0.26

30.

280

0.12

7ag

e2em

plo

yees

aged

29-3

6as

apro

por

tion

ofal

lem

plo

yees

0.25

50.

259

0.12

00.

250

0.25

70.

121

0.29

60.

300

0.09

0ag

e3em

plo

yees

aged

37-4

7as

apro

por

tion

ofal

lem

plo

yees

0.20

00.

204

0.10

90.

200

0.20

40.

110

0.22

20.

219

0.07

9ag

e4em

plo

yees

aged

47-6

5as

apro

por

tion

ofal

lem

plo

yees

0.25

10.

212

0.12

40.

252

0.17

80.

150.

232

0.16

20.

067

skill1

emplo

yees

wit

hco

mpuls

ory

educa

tion

asa

pro

por

tion

ofal

lem

plo

yees

0.17

60.

271

0.12

90.

164

0.27

20.

128

0.20

10.

238

0.12

3sk

ill2

emplo

yees

wit

ha

seco

ndar

y/

pos

t-se

condar

yed

uca

tion

asa

pro

por

tion

ofal

lem

plo

yees

0.71

40.

689

0.18

90.

714

0.69

00.

189

0.65

80.

662

0.14

7sk

ill3

emplo

yees

wit

ha

tert

iary

educa

tion

asa

pro

por

tion

ofal

lem

plo

yees

00.

040

0.09

90

0.03

80.

097

0.04

30.

100

0.13

7te

nure

aver

age

tenure

4.47

34.

622

1.86

74.

466

4.61

61.

871

5.03

85.

025

1.59

6m

anag

erm

anag

ers

asa

pro

por

tion

ofal

lem

plo

yees

0.01

80.

045

0.06

40.

016

0.04

50.

064

0.03

70.

052

0.05

9m

iddle

man

ager

mid

dle

man

ager

sas

apro

por

tion

ofal

lem

plo

yees

0.83

70.

762

0.24

10.

842

0.76

40.

240

0.65

80.

599

0.24

0blu

eco

llar

sblu

eco

llar

sas

apro

por

tion

ofal

lem

plo

yees

0.14

00.

236

0.34

80.

140

0.23

40.

348

00.

384

0.48

6si

ze1

1,if

the

firm

has

less

than

50em

plo

yees

10.

816

0.38

71

0.82

50.

379

00.

154

0.31

6si

ze2

1,if

the

firm

has

btw

50an

dem

plo

yees

00.

096

0.29

50

0.09

30.

291

00.

416

0.49

8si

ze3

1,if

the

firm

has

mor

eth

an50

emplo

yees

00.

087

0.28

10

0.08

00.

272

00.

056

0.32

4In

dex

Eth

nic

Her

findah

ldiv

ersi

tyin

dex

bas

edon

emplo

yees

’nat

ional

ity

00.

095

0.20

20

0.08

70.

194

0.34

00.

299

0.27

8In

dex

Skill

Her

findah

ldiv

ersi

tyin

dex

bas

edon

emplo

yees

’sk

ills

0.40

60.

369

0.14

80.

402

0.36

70.

148

0.47

20.

437

0.13

1In

dex

Dem

oH

erfindah

ldiv

ersi

tyin

dex

bas

edon

emplo

yees

’dem

ogra

phic

char

acte

rist

ics

0.76

20.

748

0.08

20.

760

0.74

60.

081

0.80

40.

795

0.05

5A

ccou

nti

ng

Var

iable

s:P

aten

tap

plica

tion

san

nual

num

ber

ofpat

ent

applica

tion

s0

0.02

90.

628

00

00

0.82

93.

142

capit

al(1

000

kr.

)11

334.

2973

542.

3684

1303

.410

864

5701

5.39

7814

29.8

7771

4.73

5412

78.6

2071

364

fore

ign-o

wner

ship

1,if

the

firm

isfo

reig

now

ned

00.

004

0.06

60

0.00

50.

066

00.

004

0.06

1m

ult

i1,

ifth

efirm

ism

ult

i-es

tablish

men

t0

0.09

30.

260

00.

093

0.29

10

0.29

80.

457

exp

1,if

the

firm

isex

por

ting

10.

506

0.49

90

0.48

80.

499

10.

874

0.33

1ge

osp

illo

ver

spillo

ver

vari

able

bas

edon

the

tech

nol

ogic

aldis

tance

40.1

925

226.

697

456.

646

40.1

9252

228.

2731

228.

2731

1130

.534

1063

.769

362.

0997

tech

spillo

ver

spillo

ver

vari

able

bas

edon

the

geog

raphic

aldis

tance

1091

.168

1031

.535

345.

931

1090

.384

1030

.382

345.

2853

50.0

8433

182.

6429

340.

2594

N10

7536

1032

2443

12

Not

es:

:A

llw

orkf

orce

com

posi

tion

and

acco

unti

ngva

riab

les

are

expr

esse

das

tim

eav

erag

esfr

om19

95to

2003

.T

hein

dust

rial

sect

ors

incl

uded

inth

eem

piri

cala

naly

sis

are

the

follo

win

g:fo

od,

beve

rage

san

dto

bacc

o(4

.05

%);

text

iles

(2.2

4%

),w

ood

prod

ucts

(6.6

8%

),ch

emic

als

(3.4

9%

),ot

her

non-

met

allic

min

eral

prod

ucts

(1.5

0%

),ba

sic

met

als

(19.

13%

),fu

rnit

ure

(3.7

9%

),co

nstr

ucti

on(2

2.40

%),

who

lesa

letr

ade

(14.

67%

),re

tail

trad

e(9

.02

%),

post

and

tele

com

mun

icat

ions

(0.2

7%

),fin

anci

alin

term

edia

tion

(1.1

9%

)an

dbu

sine

ssac

tivi

ties

(11.

02%

).

i

Tab

le2:

The

effec

tsof

lab

ordiv

ersi

tyon

firm

pro

bab

ilit

yto

innov

ate.

Mai

nre

sult

s.

Mod

el

(1)

Mod

el

(2)

Mod

el

(3)

Mod

el

(4)

Mod

el

(5)

Mod

el

(6)

Mod

el

(7)

Mod

el

(8)

Pro

bit

Pro

bit

Pro

bit

Pro

bit

(IV

)P

rob

itP

rob

itP

rob

itP

rob

it(I

V)

Index

Eth

nic

0.02

0***

0.00

3***

0.00

1*0.

001*

*0.

008*

**0.

001*

**0.

000*

0.00

0(0

.002

)(0

.001

)(0

.000

)(0

.000

)(0

.001

)(0

.000

)(0

.000

)(0

.000

)In

dex

Skill

0.01

6***

0.00

6***

0.00

2**

0.00

3**

0.02

5***

0.01

1***

0.00

3***

0.00

4***

(0.0

03)

(0.0

01)

(0.0

01)

(0.0

02)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

01)

Index

Dem

o0.

045*

**0.

008*

*0.

001

0.00

5*0.

028*

**0.

002

0.00

00.

004

(0.0

05)

(0.0

03)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

02)

(0.0

01)

(0.0

04)

Log

(K)

0.00

1***

0.00

0***

0.00

0***

0.00

0***

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

Log

(L)

0.00

0**

0.00

0*0.

000*

*0.

000*

**(0

.000

)(0

.000

)(0

.000

)(0

.000

)L

og(fi

xed

effec

ts)

0.00

6***

0.00

6***

0.00

5***

0.00

5***

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

age1

0.00

10.

001*

0.00

00.

001

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

age2

0.00

2**

0.00

2**

0.00

2**

0.00

2**

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

age3

0.00

2*0.

001

0.00

2*0.

002*

*(0

.001

)(0

.001

)(0

.001

)(0

.001

)m

ales

-0.0

000.

000

-0.0

00-0

.000

(0.0

00)

(0.0

01)

(0.0

00)

(0.0

00)

fore

igner

s0.

000

-0.0

00-0

.000

-0.0

00(0

.001

)(0

.001

)(0

.001

)(0

.001

)ex

p0.

001*

**0.

001*

**0.

001*

**0.

001*

**(0

.000

)(0

.000

)(0

.000

)(0

.000

)sk

ill1

0.00

3**

0.00

4**

0.00

2***

0.00

2***

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

skill2

0.00

6***

0.00

5***

0.00

5***

0.00

4***

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

man

ager

0.00

10.

001

0.00

10.

001

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

mid

dle

man

ager

-0.0

01*

-0.0

010.

000

0.00

0(0

.000

)(0

.000

)(0

.000

)(0

.000

)te

nure

-0.0

00**

*-0

.000

***

-0.0

00**

-0.0

00**

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

mult

i-0

.000

0.00

0-0

.000

-0.0

00(0

.000

)(0

.000

)(0

.000

)(0

.000

)ge

o spi

llov

er-0

.000

-0.0

00-0

.000

-0.0

00(0

.000

)(0

.000

)(0

.000

)(0

.000

)te

chspi

llov

er-0

.000

***

-0.0

00**

*-0

.000

***

-0.0

00**

*(0

.000

)(0

.000

)(0

.000

)(0

.000

)In

dust

ry/s

ize/

year

dum

mie

sno

yes

yes

yes

no

yes

yes

yes

N10

5799

1007

0410

0696

1006

9610

5799

1007

0410

0696

1006

96pse

udo

R2

0.14

80.

298

0.42

40.

424

0.18

70.

324

0.42

70.

427

Not

es:

The

depe

nden

tva

riab

lein

alle

stim

atio

nsis

the

prob

abili

tyto

have

atle

ast

one

pate

ntap

plic

atio

n.M

argi

nale

ffect

sre

port

ed.

Mod

el1-

Mod

el4:

dive

rsit

yba

sed

onth

eag

greg

ate

spec

ifica

tion

.M

odel

5-M

odel

8:di

vers

ity

base

don

the

deta

iled

spec

ifica

tion

.M

odel

4an

dM

odel

8re

port

resu

lts

from

IVes

tim

atio

n.W

ald

test

son

exog

enei

ty,

p-va

lue

(Mod

el4)

=0.

453;

p-va

lue

(Mod

el8)

=0,

321.

Sign

ifica

nce

leve

ls:

***1

%,

**5%

,*1

0%.

Stan

dard

erro

rscl

uste

red

atth

efir

mle

vel.

ii

Tab

le3:

The

effec

tsof

lab

ordiv

ersi

tyon

firm

pat

ent

applica

tion

s.D

iver

sity

bas

edon

the

aggr

egat

esp

ecifi

cati

on.

Mod

el

(1)

Mod

el

(2)

Mod

el

(3)

Mod

el

(4)

Mod

el

(5)

Mod

el

(6)

Mod

el

(7)

Pois

son

Pois

son

Pois

son

Pois

son

(IV

)N

egb

inN

egb

inN

egb

inIn

dex

Eth

nic

0.38

2***

0.16

4***

0.04

4*0.

043*

0.30

8***

0.10

3***

0.02

7(0

.033

)(0

.033

)(0

.023

)(0

.024

)(0

.017

)(0

.018

)(0

.020

)In

dex

Skil

l1.

792*

**1.

502*

**1.

097*

*1.

701*

*1.

047*

**0.

850*

**0.

977*

*(0

.356

)(0

.364

)(0

.459

)(0

.700

)(0

.210

)(0

.215

)(0

.345

)In

dex

Dem

o11

.184

***

4.28

2**

0.66

15.

010

6.31

5***

2.21

2**

0.50

7(1

.834

)(1

.579

)(1

.435

)(3

.384

)(0

.717

)(0

.789

)(1

.060

)L

og(K

)4.

901*

**4.

792*

**4.

222*

**(0

.601

)(0

.620

)(0

.617

)L

og(L

)0.

837*

*0.

339

0.96

8**

(0.3

40)

(0.4

85)

(0.3

50)

Dis

cou

nte

dst

ock

ofap

pli

cati

ons

-0.0

00-0

.000

-0.0

00(0

.000

)(0

.000

)(0

.000

)L

og(fi

xed

effec

ts)

0.00

30.

005*

0.00

3(0

.002

)(0

.003

)(0

.002

)F

ixed

effec

td

um

my

0.05

3***

0.05

2***

0.05

0***

(0.0

06)

(0.0

06)

(0.0

05)

age1

0.15

40.

452

0.14

0(0

.262

)(0

.327

)(0

.212

)ag

e20.

212

0.19

50.

326*

(0.2

37)

(0.2

35)

(0.1

74)

age3

0.14

40.

051

0.18

4(0

.209

)(0

.226

)(0

.171

)m

ales

0.26

60.

717

0.02

3(0

.524

)(0

.641

)(0

.368

)fo

reig

ner

s0.

032

0.02

90.

017

(0.0

33)

(0.0

33)

(0.0

27)

exp

0.49

6***

0.46

8***

0.46

2***

(0.1

10)

(0.1

08)

(0.0

87)

skil

l12.

079*

*2.

761*

*1.

858*

*(0

.776

)(0

.942

)(0

.594

)sk

ill2

0.16

2***

0.12

2**

0.15

0***

(0.0

30)

(0.0

47)

(0.0

29)

man

ager

0.00

1-0

.007

0.03

7(0

.038

)(0

.039

)(0

.034

)m

idd

lem

anag

er-0

.389

-0.3

09-0

.370

(0.2

67)

(0.2

66)

(0.2

45)

tenu

re-0

.392

-0.4

25-0

.396

**(0

.290

)(0

.286

)(0

.200

)m

ult

i-0

.000

0.02

20.

003

(0.0

17)

(0.0

24)

(0.0

13)

geo s

pillov

er-0

.392

-1.3

40-0

.446

(0.5

80)

(0.9

91)

(0.4

84)

tech

spi

llov

er-0

.027

-0.0

09-0

.039

(0.0

38)

(0.0

40)

(0.0

38)

Ind

ust

ry/s

ize/

year

du

mm

ies

no

yes

yes

yes

no

yes

yes

N10

5799

1057

9910

5791

1057

8710

5799

1057

9910

5791

chi2

192.

411

2434

.350

404.

748

668.

460

6.2

6979

1.2

5106

4.6

Not

es:

The

depe

nden

tva

riab

lein

all

esti

mat

ions

isth

enu

mbe

rof

pate

ntap

plic

a-ti

ons.

Ela

stic

itie

sre

port

ed.

Sign

ifica

nce

leve

ls:

***1

%,*

*5%

,*10

%.

Stan

dard

erro

rscl

uste

red

atth

efir

mle

vel.

Poi

sson

(IV

):st

anda

rder

rors

are

boot

stra

pped

usin

ga

se-

quen

tial

two

step

boot

stra

ppin

gpr

oced

ure

wit

h20

0re

plic

atio

ns.

F-s

tats

onex

clud

edin

stru

men

ts:

i)In

dex

Eth

nic

atco

unty

leve

l:12

99.6

8;ii)

Inde

xsk

illat

coun

tyle

vel:

189.

68;

iii)

Inde

xde

mo

atco

unty

leve

l:22

1.79

.

iii

Tab

le4:

The

effec

tsof

lab

ordiv

ersi

tyon

firm

pat

ent

applica

tion

s.D

iver

sity

bas

edon

the

det

aile

dsp

ecifi

cati

on.

Mod

el

(1)

Mod

el

(2)

Mod

el

(3)

Mod

el

(4)

Mod

el

(5)

Mod

el

(6)

Mod

el

(7)

Pois

son

Pois

son

Pois

son

Pois

son

(IV

)N

egb

inN

egb

inN

egb

inIn

dex

Eth

nic

0.45

3***

0.23

2***

0.07

0**

0.04

50.

323*

**0.

124*

**0.

043

(0.0

41)

(0.0

39)

(0.0

32)

(0.0

44)

(0.0

21)

(0.0

24)

(0.0

28)

Ind

exS

kil

l4.

825*

**5.

426*

**1.

911*

**2.

234*

**3.

556*

**3.

987*

**1.

900*

**(0

.476

)(0

.615

)(0

.503

)(0

.627

)(0

.250

)(0

.376

)(0

.392

)In

dex

Dem

o14

.568

***

3.86

4**

-0.0

274.

612

7.71

1***

1.27

6-0

.161

(2.5

03)

(1.8

69)

(1.5

68)

(4.9

97)

(0.8

85)

(0.8

85)

(1.1

59)

Log

(K)

4.75

6***

4.82

1***

4.05

5***

(0.6

01)

(0.5

99)

(0.6

04)

Log

(L)

0.86

6**

0.93

2**

1.01

2**

(0.3

39)

(0.3

43)

(0.3

46)

Dis

cou

nte

dst

ock

ofap

pli

cati

ons

-0.0

00-0

.000

-0.0

00(0

.000

)(0

.000

)(0

.000

)L

og(fi

xed

effec

ts)

0.00

30.

003

0.00

3(0

.002

)(0

.002

)(0

.002

)F

ixed

effec

td

um

my

0.05

2***

0.05

2***

0.04

9***

(0.0

06)

(0.0

06)

(0.0

05)

age1

0.12

70.

186

0.09

9(0

.253

)(0

.254

)(0

.203

)ag

e20.

175

0.21

50.

274*

(0.2

34)

(0.2

29)

(0.1

71)

age3

0.18

30.

233

0.20

4(0

.200

)(0

.194

)(0

.166

)m

ales

0.36

20.

383

0.13

0(0

.512

)(0

.502

)(0

.364

)fo

reig

ner

s0.

020

0.00

90.

005

(0.0

33)

(0.0

34)

(0.0

27)

exp

0.49

4***

0.48

6***

0.45

7***

(0.1

10)

(0.1

07)

(0.0

87)

skil

l10.

896*

*0.

844*

0.91

7**

(0.4

48)

(0.4

33)

(0.3

55)

skil

l20.

147*

**0.

144*

**0.

134*

**(0

.028

)(0

.028

)(0

.028

)m

anag

er0.

021

0.02

30.

058*

(0.0

37)

(0.0

35)

(0.0

33)

mid

dle

man

ager

0.07

60.

166

0.13

1(0

.291

)(0

.296

)(0

.256

)te

nu

re-0

.362

-0.3

23-0

.351

*(0

.291

)(0

.294

)(0

.199

)m

ult

i0.

004

0.00

50.

007

(0.0

17)

(0.0

16)

(0.0

13)

geo s

pillov

er-0

.635

-0.6

91-0

.838

*(0

.579

)(0

.598

)(0

.489

)te

chspi

llov

er-0

.029

-0.0

47-0

.044

(0.0

39)

(0.0

41)

(0.0

39)

Ind

ust

ry/s

ize/

year

du

mm

ies

no

yes

yes

yes

no

yes

yes

N10

5799

1057

9910

5791

1057

8710

5799

1057

9910

5791

chi2

214.

685

033.

944

697.

043

060.

768

7.2

5655

9.4

4979

6.4

Not

es:T

hede

pend

ent

vari

able

inal

les

tim

atio

nsis

the

num

ber

ofpa

tent

appl

ica-

tion

s.E

last

icit

ies

repo

rted

.Si

gnifi

canc

ele

vels

:**

*1%

,**5

%,*

10%

.St

anda

rder

rors

clus

tere

dat

the

firm

leve

l.P

oiss

on(I

V):

stan

dard

erro

rsar

ebo

otst

rapp

edus

ing

ase

-qu

enti

altw

ost

epbo

otst

rapp

ing

proc

edur

ew

ith

200

repl

icat

ions

.F

-sta

tson

excl

uded

inst

rum

ents

:i)

Inde

xE

thni

cat

coun

tyle

vel:

1585

.02;

ii)In

dex

skill

atco

unty

leve

l:84

0.97

;iii

)In

dex

dem

oat

coun

tyle

vel:

483.

28.

iv

Table 5: The effects of labor diversity on the probability of applying in different tech-nological areas.

Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7) Model (8)Probit Probit Probit Probit (IV) Probit Probit Probit Probit (IV)

Index Ethnic 0.162** 0.109* 0.020 0.044 0.171*** 0.142** 0.040 0.050(0.057) (0.064) (0.060) (0.060) (0.046) (0.052) (0.053) (0.065)

Index Skill 0.572*** 0.563*** 0.988*** 1.412*** 0.821*** 0.809*** 0.488** 0.476**(0.129) (0.156) (0.299) (0.392) (0.143) (0.193) (0.209) (0.203)

Index Demo 0.568* 0.217 0.433 1.070 0.525* 0.073 0.299 0.099(0.316) (0.309) (0.364) (0.823) (0.316) (0.348) (0.370) (1.358)

Log(K) 0.030** 0.029** 0.030** 0.031**(0.012) (0.012) (0.012) (0.012)

Log(L) 0.014 -0.009 0.014 0.016(0.023) (0.029) (0.024) (0.024)

Log(fixed effects) 0.250*** 0.278*** 0.260*** 0.258***(0.045) (0.052) (0.046) (0.046)

age1 0.712*** 0.800*** 0.717*** 0.758***(0.184) (0.213) (0.181) (0.182)

age2 0.739*** 0.695*** 0.696*** 0.713***(0.200) (0.202) (0.198) (0.202)

age3 0.287 0.198 0.404 0.382(0.286) (0.294) (0.276) (0.265)

males 0.143 0.195 0.125 0.125(0.099) (0.127) (0.099) (0.098)

foreigners 0.231 0.175 0.189 0.062(0.258) (0.257) (0.274) (0.275)

exp 0.004 0.001 0.016 0.016(0.041) (0.040) (0.042) (0.041)

skill1 0.731** 1.031*** 0.078 0.039(0.253) (0.305) (0.145) (0.140)

skill2 0.151 -0.175 0.202 0.109(0.202) (0.256) (0.201) (0.206)

manager 0.171 0.203 0.227 0.277(0.214) (0.205) (0.229) (0.229)

middle manager 0.016 0.035 0.064 0.086(0.091) (0.090) (0.100) (0.100)

tenure -0.003 -0.004 -0.004 -0.003(0.010) (0.010) (0.010) (0.010)

multi 0.006 0.044 0.005 0.009(0.035) (0.047) (0.037) (0.037)

co-patent 0.024 0.030 0.009 0.000(0.032) (0.032) (0.021) (0.000)

geospillover 0.000 -0.000 0.000 -0.000(0.000) (0.000) (0.000) (0.000)

techspillover -0.000 -0.000 -0.000 0.013(0.000) (0.000) (0.000) (0.021)

Industry/size/year dummies no yes yes yes no yes yes yesN 1146 1116 1116 1116 1146 1116 1116 1116pseudo R2 0.068 0.146 0.378 0.332 0.100 0.159 0.371 0.324

Notes: The dependent variable in all estimations is the probability of applying apatent in different technological areas. Marginal effects reported. Model1-Model4:diversity based on the aggregate specification. Model5-Model8: diversity based on thedetailed specification. Model4 and Model8 report results from IV estimation. Waldtests on exogeneity, p-value (Model4)=0.657; p-value (Model8)=0.823. Significancelevels: ***1%, **5%, 10*%. Standard errors clustered at the firm level.

v

Table 6: The effects of labor diversity on firm probability to innovate. Robustnesschecks.

(1) (2) (3) (4) (5)Occupation specific diversity Shannon entropy index Richness index Grants-based definition of innovationWhite collar Blue collar

Index Ethnic Aggr 0.001** -0.000 0.000 0.000** 0.000(0.000) (0.000) (0.000) (0.000) (0.000)

Index Skill Aggr 0.001** 0.001 0.001** 0.001*** 0.003**(0.000) (0.000) (0.000) (0.000) (0.001)

Index Demo Aggr 0.000 -0.000 0.000 0.000 0.002*(0.000) (0.000) (0.000) (0.000) (0.001)

N 100696 100696 105791 100696pseudo R2 0.424 0.425 0.368 0.646

Index Ethnic Disaggr 0.000* 0.000 0.000 0.000** 0.000*(0.000) (0.000) (0.001) (0.000) (0.000)

Index Skill Disaggr 0.001** 0.001** 0.003*** 0.001*** 0.003***(0.000) (0.000) (0.001) (0.000) (0.001)

Index Demo Disaggr -0.000 -0.000 0.000 0.000 0.000(0.000) (0.000) (0.001) (0.000) (0.001)

N 100696 100696 105791 100696pseudo R2 0.425 0.425 0.371 0.427

Notes: The dependent variable in all estimations is the probability to have at leastone patent application. Marginal effects reported. All regressions include all the firmspecific characteristics, year and three-digit industry dummies. Significance levels:***1%, **5%, *10%. Standard errors clustered at the firm level.

Table 7: The effects of labor diversity on firm patents. Robustness checks.

(1) (2) (3) (4) (5)Occupation specific diversity Shannon entropy index Richness index Grants-based definition of innovationWhite collar Blue collar

Index Ethnic Aggr 0.033* 0.007 0.099 0.034 0.044*(0.019) (0.004) (0.080) (0.022) (0.023)

Index Skill Aggr 0.422** 0.065 0.811* 0.191** 1.097**(0.173) (0.057) (0.445) (0.082) (0.459)

Index Demo Aggr -0.465 0.131 0.195 0.334 0.661(0.427) (0.114) (0.527) (0.321) (1.435)

N 105791 105791 105791 105791chi2 60480.9 53127.0 3147.2 50404.7

Index Ethnic Disaggr 0.051** 0.014* 0.090** 0.015*** 0.027(0.026) (0.008) (0.041) (0.003) (0.020)

Index Skill Disaggr 0.739*** 0.197 0.672** 1.282*** 0.977**(0.193) (0.133) (0.257) (0.186) (0.345)

Index Demo Disaggr -0.691* 0.074 -0.058 0.325 0.507(0.355) (0.165) (0.461) (0.325) (1.060)

N 105791 105791 105791 105791chi2 61299.9 56458.7 3509.1 51064.6

Notes: The dependent variable in all estimations is the probability to have at leastone patent application. Elasticities reported. All regressions include all the firmspecific characteristics, year and three-digit industry dummies. Significance levels:***1%, **5%, *10%. Standard errors clustered at the firm level.

vi

Figure 1: Commuting areas,1995, Denmark.

 

vii