Education, Training and Networking for Entrepreneurship in ...

Education, Training and Networking 

for Entrepreneurship in Denmark 2008 

– studied via Global Entrepreneurship Monitor 

Thomas Schøtt

Education, Training and Networking 

for Entrepreneurship in Denmark 2008 

– studied via Global Entrepreneurship Monitor 

Thomas Schøtt 

University of Southern Denmark 

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© Thomas Schøtt (and GERA for items quoted from GEM surveys). 

University of Southern Denmark, Centre for Entrepreneurship and Small Business Studies 

First edition 2009, first printing 2009 

ISBN 978-87-91070-32-7 

CESFO Report no XX 

Printed in Denmark by Jørn Thomsen Offset A/S. 

Cover: Birgitte Krog, Jørn Thomsen Offset A/S, and Frk. Frost 

Copying from this book, in whole or in part, without written permission of the author is not 

permitted according to Danish copyright law. 

This publication may also be viewed on the internet at http://www.cesfo.dk/ 

Further information can also be seen at http://www.sam. sdu.dk/ansat/tsc 

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Preface 

This monograph accounts for changes in entrepreneurship in Denmark and for effects upon entrepreneurship 

from education, training, competences and networking. 

Is the level of entrepreneurial activity in Denmark increasing, constant or declining, and is the Danish 

level higher or lower than in other societies? Are the cultural and institutional framework conditions 

in Denmark improving, stable or deteriorating, and are Danish conditions better or worse than in 

other societies? How are the framework conditions shaping entrepreneurship, and what are the effects 

of culture and institutions? How are people in Denmark trained for entrepreneurship, during 

schooling and afterwards in various organizational contexts, and is training in Denmark expanding 

like in other societies? How are people’s training for entrepreneurship dependent on their gender, 

age and education, and how is their training promoting their entrepreneurial competency and networking? 

How are people’s vocation – entrepreneur or not – shaped by their education, training, 

competency and networking? How are entrepreneurs’ education and training affecting their competency 

to start a business and their networking to access resources such as advice? How are entrepreneurs’ 

education, training, competency and networking expanding their innovativeness, exports and 

growth-expectations? 

These questions are addressed here by analyzing data from our surveys in 2008 and preceding years 

in Denmark and many other countries, gathered mainly in our research program Global Entrepreneurship 

Monitor, GEM. Our up-to-date surveys and analyses provide leading indicators of current 

changes in entrepreneurship. 

This study is a sequel to my three research monographs Entrepreneurship in Denmark 2005, Entrepreneurship 

in the Regions in Denmark 2006 and Growth-Entrepreneurship in Denmark 2007, and is 

the tenth in the series of annual thematic studies of entrepreneurship in Denmark, with up-to-date 

analyses covering each year from 1999 to 2008. The series is part of the research program Global 

Entrepreneurship Monitor (GEM), conducted by the international consortium Global Entrepreneurship 

Research Association (GERA), in which the Danish team is headed by the author. The Danish 

team and the consortium have collected most of the data, but the analyses and interpretations are of 

course the sole responsibility of the author. This study is accompanied by a Danish anthology on 

entrepreneurial competences, Entreprenørskab og kompetencer. 

The study was supported mainly by a grant from the International Danish Entrepreneurship Academy 

(IDEA). The University of Southern Denmark – through our Department of Entrepreneurship and Relationship 

Management and our Centre for Entrepreneurship and Small Business Studies – granted 

me time and facilities for the research. The study benefitted from discussions with Torben Bager, Kim 

Klyver, Hannes Ottósson, Kent Wickstrøm Jensen and our numerous colleagues in GEM/GERA. 

This publication can also be viewed on the internet at www.sdu.dk/om_sdu/institutter_centre/ 

c_smaavhs.aspx and at www.gemconsortium.org and also at www.sam.sdu.dk/ansat/tsc where details 

of the study are published. 

University of Southern Denmark, Winter solstice 2008 


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Contents 

Preface .............................................................................................................. 3 

Contents .............................................................................................................. 5 

Sammenfatning (Danish summary)............................................................................... 7 

PART 1 INTRODUCTION................................................................................................. 13 

Chapter 1 Is entrepreneurship expanding through education, training, competency ........ 13 

and networking? 

Chapter 2 Researching entrepreneurship in Denmark and other societies ...................... 16 

PART 2 SOCIETY........................................................................................................... 19 

Chapter 3 National entrepreneurial activity: trends in Denmark and comparisons to 

other societies......................................................................................... 19 

Chapter 4 National conditions for entrepreneurship: trends in Denmark and 

comparisons to other societies................................................................... 23 

Chapter 5 National dynamics: effects of conditions upon entrepreneurial activity............ 37 

PART 3 PEOPLE............................................................................................................. 43 

Chapter 6 People’s training for entrepreneurship in Denmark and other societies............ 43 

Chapter 7 People’s education, training, competency and networking in Denmark............ 50 

Chapter 8 People’s vocation expanded by education, training, competency and ............. 54 

networking in Denmark 

PART 4 ENTREPRENEURS................................................................................................ 59 

Chapter 9 Entrepreneurs’ education, training, competency and networking in Denmark... 59 

Chapter 10 Entrepreneurs’ innovativeness expanded by education, training, competency 

and networking........................................................................................ 66 

Chapter 11 Entrepreneurs’ exports expanded by education, training, competency and 

networking.............................................................................................. 70 

Chapter 12 Entrepreneurs’ growth-expectations expanded by education, training, 

competency and networking...................................................................... 74 

PART 5 CONCLUSIONS................................................................................................... 79 

Chapter 13 Expanding entrepreneurship through education, training, competency and 

networking.............................................................................................. 79 

Appendix with technical specifications.......................................................................... 85 

Bibliography .............................................................................................................. 97 

National Teams in 2008 in Global Entrepreneurship Monitor........................................... 103 

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Sammenfatning 

’Entreprenørskab’ betegner den entreprenante 

aktivitet der består i at opdage og udnytte forretningsmuligheder 

ved at starte og drive virksomhed. 

Det omfatter dermed også ’intraprenørskab’ 

der betegner opdagelse af muligheder 

undervejs i driften af eksisterende virksomheder 

og udnyttelsen deraf ved at starte og drive 

en ny enhed. Aktiviteten ’opstart’ kaldes nu ofte 

iværksætteri, mens aktiviteten ’drift af virksomhed’ 

ofte kaldes ejerledelse eller selvstændig 

erhvervsdrift. Betegnelsen entreprenørskab dækker 

hermed både iværksætteri og ejerledelse eller 

selvstændighed. 

Entreprenørskab er en aktivitet der udføres som 

en erhvervsbeskæftigelse, der egentlig bør betegnes 

entreprenør efter det oprindelige franske 

entrepreneur, der i angelsaksiske samfund 

er blevet til betegnelsen for denne beskæftigelse. 

Entreprenør sammenfatter hermed de beskæftigelser, 

der ofte betegnes iværksætter, 

ejer-leder og selvstændig erhvervsdrivende. 

Entreprenørskab er blevet etableret og institutionaliseret 

som en samfundsinstitution med 

institutionelle og kulturelle rammer. Den kulturelle 

værdi, der af samfundet tillægges entreprenørskab, 

kommer kulturelt til udtryk i prestige 

af entreprenør som erhvervsbeskæftigelse, 

i anerkendelse af veludført entreprenant virke 

og også i undervisning i entreprenørskab. 

Institutionaliseringen af entreprenørskab foregår 

også i verdenssamfundet, hvor et verdensomspændende 

netværk af indflydelsesrige organisationer, 

centreret omkring OECD, Verdensbanken 

og de Forenede Nationer (især FNs organisation 

for industriel udvikling UNIDO), rådgiver 

om rammer der kan fremme entreprenørskab, 

også i Danmark (OECD 2008). Den verdensomspændende 

institutionalisering af entreprenørskab 

er et led i moderniseringen, udviklingen 

og globaliseringen af menneskeheden. 

Siden 1990erne er den globale institutionalisering 

af entreprenørskab også fremmet af en 

international sammenslutning af forskere, nu 

formaliseret som Global Entrepreneurship Research 

Association (GERA), især gennem sammenslutningens 

forskningsprogram Global Entrepreneurship 

Monitor (GEM), som dette studie 

og forfatteren er tilknyttet, som National 

Team Leader for det danske forskerteam. 

I forskningsprogrammet Global Entrepreneurship 

Monitor gennemfører vi hvert år en spørgeskemaundersøgelse 

af voksenbefolkningen i 

hvert deltagende land og også en ekspertvurdering 

af landets rammevilkår (kapitel 2). Op til 

2008, siden begyndelsen i 1999, har 66 lande 

deltaget i et eller flere år. Danmark har deltaget 

i hvert af de ti år, så vi opdaterer løbende og ser 

de aktuelle udviklingstendenser. Ved sammenligninger 

af mange lande kan vi studere den generelle 

dynamik i entreprenørskab, som den 

typisk er i mange lande, især i de højt udviklede 

lande, og mere specifikt den danske dynamik. 

Læring, som den kan opnås gennem uddannelse, 

træning og netværk, er i fokus. Det store 

spørgsmål er: Hvordan ekspanderer entreprenørskab 

gennem uddannelse, træning, kompetence 

og netværk? Spørgsmålet præciseres ved 

at fokusere på befolkningen og på entreprenørerne: 

Hvordan ekspanderer befolkningen involveringen 

i entreprenørskab gennem deres læring? 

Hvordan ekspanderer entreprenører deres 

innovation, eksport og vækstforventninger gennem 

deres læring? 

Dog bringes først en analyse af entreprenør- 

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skab i samfundet, både i Danmark og i andre 

udviklede lande, opdateret til midten af 2008, 

lige før den nuværende krise for alvor slog igennem. 

Krisen var begyndt da vi omkring maj 

2008 gennemførte vores nyeste spørgeskemaundersøgelser 

i den danske befolkning og i et 

panel af danske eksperter omkring entreprenørskab, 

og den begyndende krise har muligvis påvirket 

folks holdninger og adfærd og eksperters 

vurderinger. 

Iværksætteri i samfundet – Danmark og 

andre lande 

Hvad er niveuaet i entreprenørskab i Danmark? 

Mere specifikt søger kapitel 3 at besvare spørgsmålene: 

Er udviklingstendensen i niveauet opadgående, 

stabil, eller nedadgående? Hvor højt 

er niveauet i Danmark sammenlignet med andre 

udviklede lande? 

Det danske niveau af iværksætteri i opstartsfasen, 

målt som voksenbefolkningens rate af 

iværksættere der er i færd med at starte eller 

lige har startet en virksomhed, er omtrent som 

det typisk er for de udviklede lande. Igennem 

adskillige år, og nu fortsat, er Danmark omkring 

midten blandt de udviklede lande i iværksætteraktivitet 

i voksenbefolkningen. Men når vi 

måler den allertidligste fase, så ser vi i Danmark 

et fald i raten af folk der har til hensigt at starte 

ny virksomhed inden for de næste få år. Dette 

fald i intentioner i Danmark kan betragtes som 

et forvarsel om et kommende fald i opstartsraten. 

Det observerede fald i befolkningens intentioner 

går endda forud for den økonomiske krise 

der kom i 2008. 

Hvordan er de nationale rammevilkår for entreprenørskab 

i Danmark? Mere specifikt søger kapitel 

4 at besvare spørgsmålene: Er kulturelle og 

institutionelle vilkår i Danmark under forbedring 

eller forringelse? Hvor gode er vilkårene i Danmark 

sammenlignet med betingelserne i andre 

lande? 

I 2008 og i de tidligere år har vi ved ekspertvurderinger 

opnået indikatorer på 14 rammevilkår i 

Danmark, og ligeså i andre lande. De omfatter 4 

kulturelle vilkår: individualisme som en kulturel 

værdi, prestige af iværksætteren som erhvervsbekæftigelse, 

uddannelse og træning i entreprenørskab, 

befolkningens færdigheder i entreprenørskab, 

samt 10 institutionelle vilkår: mulighederne 

for entreprenørskab, finansielle ressourcer, 

regeringspolitik for entreprenørskab, 

offentlige støtteprogrammer, overførsel af teknologi 

til erhvervslivet, kommerciel og juridisk 

infrastruktur, markedets åbenhed for ny virksomhed, 

den teknisk-fysiske infrastruktur, ophavsrettigheder 

og patentsystem, samt støtteforanstaltninger 

til vækst-entreprenørskab. 

De fleste af rammevilkårene i Danmark er blevet 

bedre igennem de senere år (dog måske ikke i 

2008). Nogle få af vilkårene er stagnerende. Ingen 

af vilkårene er alvorligt forringede igennem 

de senere år. Alt i alt er rammen for dansk iværksætteri 

igennem de senere år blevet mere favorabel. 

Til trods for forbedringerne, så er vilkårene i 

Danmark stadig omkring midten i sammenligninger 

med de andre udviklede lande. De fleste 

rammevilkår i Danmark er som de typisk er i de 

udviklede lande, enkelte vilkår er betydeligt 

bedre i Danmark, og enkelte vilkår er betydeligt 

ringere i Danmark end typisk blandt de udviklede 

lande, så alt i alt er rammen i Danmark ret 

typisk. 

Folks uddannelse, træning, kompetence og netværk 

har hver en effekt på deres erhvervsbeskæftigelse 

– hvorvidt de er entreprenører. Folk 

med lang højere uddannelse er særlig tilbøjelige 

til at blive entreprenører. Trænede folk er oftere 

entreprenører end utrænede folk, kompetente 

folk er oftere entreprenører end mindre 

kompetente folk, og folk der netværker er oftere 

entreprenører end folk der ikke netværker med 

en starter. At lang uddannelse, træning, kompetence 

og netværk fremmer tilbøjeligheden til at 

være entreprenører overrasker nok ikke, men 

det er væsentligt at se at hver bidrager til at ekspandere 

befolkningens involvering i entreprenørskab. 

Disse effekter på folks erhvervsbeskæftigelse 

vises i Figur A. 

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Figur A 

Folks erhvervsbeskæftigelse formet af deres uddannelse, træning, kompetence og netværk. 

Voksne i Danmark 

Entreprenører har oftere end andre folk en lang 

videregående uddannelse, og entreprenører 

har mindre ofte end andre hverken erhvervsuddannelse 

eller lang videregående uddannelse. 

Entrepreneører er typisk ikke trænede i at starte 

ny virksomhed, om end de oftere end andre folk 

er trænede, men mange andre har faktisk modtaget 

træning i at starte ny virksomhed. Dette 

indikerer at der er trænede ikke-entrepre nører i 

befolkningen som kunne motiveres til iværksætteri. 

Hvordan er entreprenørers innovation påvirket 

af deres uddannelse, træning, kompetence og 

netværk? Mere specifikt addresserer kapitel 10 

spørgsmålene: Hvad er effekterne på innovation 

Erhvervsbeskæftigelse 

Netværk 

Kompetence 

Træning 

Uddannelse 

Entreprenører ekspanderer gennem læring, innovation, 

eksport og vækstforventninger 

Hvad er danske entreprenørers uddannelse, 

træning, kompetence og netværk? Mere specifikt 

adresserer kapitel 9 spørgsmålene: Er entreprenører 

uddannet i forskellige retninger end 

andre folk? Er entreprenører typisk trænede i at 

starte ny virksomhed, mens andre typisk er 

utrænede? Har entreprenører typisk kompetence 

til at starte ny virksomhed, mens andre folk 

typisk ikke har denne kompetence? Netværker 

entreprenører typisk med en bred vifte af formelle 

og uformelle rådgivere? Hvordan påvirker 

entreprenørers uddannelse, træning, kompetence 

og netværk hinanden? 

9

Figur B 

Entreprenørers ekspansivitet formet af deres uddannelse, træning, kompetence og netværk 

Startere og ejerledere i Danmark 

Ekspansivitet 

Netværk 

Kompetence 

Træning 

Uddannelse 

fra uddannelse, fra træning, fra kompetence og 

fra netværk? 

Entreprenørers (her startere og ejerledere) innovation 

formes af effekter fra deres uddannelse, 

træning, kompetence og netværk på de 

måder at entreprenører med lang videregående 

uddannelse, med træning, med kompetence og 

med store netværk af formelle og uformelle 

rådgivere tenderer til at være særlig innovative. 

kapitel 11 spørgsmålene: Hvad er effekterne på 

eksport fra uddannelse, fra træning, fra kompetence 

og fra netværk? 

Entreprenørers (her startere og ejerledere) eksport-orientering 

formes af særskilte effekter fra 

deres uddannelse, træning, kompetence og netværk 

på de måder at de særlig eksport-orienterede 

entreprenører ofte er de med lang videregående 

uddannelse, med træning, med kompetence, 

og med store netværk af rådgivere. 

Hvordan er entreprenørers eksport-orientering 

formet af deres uddannelse, træning, kompetence 

og netværk? Mere specifikt addresserer 

Hvordan er entreprenørers forventning om vækst 

formet af deres uddannelse, træning, kompetence 

og netværk? Mere specifikt addresserer 

10

kapitel 12 spørgsmålene: Hvad er effekterne på 

vækstforventning fra uddannelse, fra træning, 

fra kompetence og fra netværk? 

Entreprenørers (her startere og ejerledere) 

vækstforventning påvirkes af deres uddannelse, 

træning, kompetence og netværk på de måder 

at entreprenører med lang videregående 

uddannelse, med træning, med kompetence, 

og med omfattende netværk ofte har særlig 

høje vækstforventninger. 

Expansiv entreprenørskab betegner her den entreprenørskab 

der er særlig innovativ, eksporterende 

og vækstforventende. Vi vil så sige at entreprenørskab 

er ekspansiv i den grad den er 

innovativ, eksporterende og vækstforventende. 

Ligeledes kalder vi en entreprenør ekspansiv i 

den udstrækning entreprenøren innoverer, eksporterer 

og forventer vækst. 

Hvorvidt entreprenører er ekspansive afhænger 

af deres uddannelse, træning, kompetence og 

netværk, på de måder at entreprenører med 

lang videregående uddannelse, med træning, 

med kompetence, og med store netværk tenderer 

til at være særlig ekspansive. 

Disse effekter på ekspansivitet afbildes i Figur 

B, hvor en positiv effekt vises som en fuldt optrukken 

pil, en negativ effekt som en stiplet pil, 

og størrelsen af effekten indikeres ved pilens 

tykkelse. 

Entreprenørskab ekspanderer således gennem 

uddannelse, træning, kompetence og netværk 

på to planer: befolkningen ekspanderer deltagelsen 

i entreprenørskab ved at starte og drive 

virksomhed, og entreprenører ekspanderer innovation, 

eksport og vækstforventninger på 

grundlag af deres uddannelse, træning, kompetence 

og netværk. 

11

Part 1 

Introduction 

Chapter 1 

Is entrepreneurship expanding through education, training, competency 


Once upon a time, entrepreneurship was considered 

something people might do if they were 

gifted and inspired. Success was considered 

the result of a gift, received at birth, the result of 

bringing the gift to fruition, and the successful 

business might bring a glorious reputation to 

its founder. Failure was often attributed to lack 

of such a gift, and the failure would bring disrepute 

to the starter. Considered a matter of gift 

and inspiration, entrepreneurship did not involve 

learning. 

The conception of entrepreneurship changed. 

Entrepreneurship came to be considered something 

that can be learned. Learning could come 

through trial and error in solitude. Success was 

considered the result of hard work, through a 

series of trials and errors, conducted in solitude. 

Considered something learnable by experiencing 

trials and errors in solitude, entrepreneurship 

did not involve instruction or learning from 

others. 

The conception is changing again. Nowadays, 

entrepreneurship is considered learnable, both 

as codified knowledge that can be acquired 

through formal instruction and education and 

as tacit knowledge that can be acquired as advice, 

as know-how and as experiential learning, 

for example as apprentice in a business. 

Educators and policy-makers are now busy creating 

and extending instruction in entrepreneurship, 

so that more levels and kinds of education 

will include instruction in entrepreneurship, 

so that more pupils, students and adults 

will learn entrepreneurship, so that they will 

learn more elements of entrepreneurship, not 

only things like developing a business plan, 

planning production and targeting sales, but 

also the very inception of the entrepreneurial 

process such as nurturing motivation, pursuing 

interests, generating bold ideas, elaborating 

sketches and evaluating feasibilities. 

Entrepreneurship can be conceptualized as the 

activity that comprises discovering opportunities 

and the exploitation of opportunities in existing 

and new firms. The activity is now institutionalized 

and supported in society, with a social 

role for performing the activity, the vocation 

called the entrepreneur (Christensen 2005, 

Swedberg 2000). It may therefore be fruitful to 

focus on the people who may become involved 

and perform the activity and the learning affecting 

their involvement and performance. 

I here investigate people’s learning through 

their education in entrepreneurship, their training 

in starting a business, and their networking 

through which they may learn, and investigate 

how people’s learning expands their involvement 

in entrepreneurship and how entrepreneurs’ 

learning expands their innovation, exports 

and expectations for growth. 

The monograph first considers the methods for 

the investigation, 

How can we research entrepreneurship? 

More specifically, Chapter 2 addresses the 

methodological problem, 

13

How are we investigating entrepreneurship 

at the levels of society, people, and performers? 

Society is the focus of Part 2, 

What is the level of entrepreneurial activity in 

Denmark? 

More specifically, Chapter 3 addresses the issues, 

Is the trend upward, stable or downward? 

How does the level in Denmark compare to 

the levels in other societies? 

How is the national framework of conditions for 

entrepreneurship in Denmark? 

More specifically, Chapter 4 answers the 

questions, 

Are cultural and institutional conditions in 

Denmark improving or deteriorating? 

How does the framework in Denmark compare 

to the framework in other societies? 

What are the national dynamics of entrepreneurship? 


questions, 

How are the cultural conditions affecting 

one another? 

How are the institutional conditions interrelated? 

How are the cultural and institutional conditions 

affecting entrepreneurship? 

People become the focus in Part 3, 

How are people trained for entrepreneurship in 

Denmark and other societies? 


problems, 

How much of the adult population is trained 

to start a business? 

Have people been trained mainly during 

their schooling or later in life? 

What is the organizational context of later 

training, and is this voluntary or compulsory? 

How is people’s training affected by their 

gender, age and education? 

Is the training expanding in Denmark and in 

other societies? 

How are people learning entrepreneurship, not 

only through training, but also through their 

education, competency and networking? 

More specifically, Chapter 7 deals with the issues, 

Are people in Denmark educated at high and 

diverse levels in the educational system? 

Do people typically have the competency to 

start a business? 

Is people’s competency enhanced by their 

education and training? 

Are people typically networking with starters? 

Is people’s networking promoted by their 

education, training and competency? 

How is people’s involvement in entrepreneurship 

shaped and expanded by their learning? 


questions, 

How is people’s vocation – entrepreneur or 

not – affected by their education, by their 

training, by their competency and by their 

networking? 

Entrepreneurs become the focus in Part 4, 

What are Danish entrepreneurs’ learning? 


questions, 

Do entrepreneurs have educations similar to 

or different from other peoples’ educations? 

Do entrepreneurs typically have training in 

starting a business, whereas other people 

typically do not have such training? 

Do entrepreneurs typically have competency 

to start a business, whereas other people 

typically do not have such competency? 

Are entrepreneurs typically networking with 

a wide spectrum of advisors? 

What are the effects among entrepreneurs’ 

education, training, competency and networking? 

14

How is entrepreneurs’ innovativeness expanded 

by their learning? 


questions, 

What are the distinct effects upon innovativeness 

from education, from training, from 

competency and from networking? 

How is entrepreneurs’ exporting expanded by 

their learning? 


questions, 

What are the separate effects upon exportorientation 

from education, from training, 

from competency and from networking? 

How are entrepreneurs’ growth-expectations 

expanded by learning? 


questions, 

What are the separate effects upon growthexpectations 

from education, from training, 

from competency and from networking? 

Finally, the answers to the above questions enable 

us to draw conclusions, in Part 5, 

How is entrepreneurship expanding through 

learning? 

More specifically, Chapter 13 draws conclusions 

to the two questions, 

How are people, through learning, expanding 

involvement in entrepreneurship? 

How are entrepreneurs, through learning, 

expanding innovation, exports and growth? 

Before investigating entrepreneurship, we shall 

first consider how this endeavour can be researched. 

15

Chapter 2 

Researching entrepreneurship in Denmark and other societies 

How can we research entrepreneurship? More 

specifically, this chapter addresses the methodological 

problem, How are we investigating 

entrepreneurship at the levels of society, people, 

and performers? 

Entrepreneurship is conceptualized as an activity 

that is institutionalized with a supportive 

framework in society, and with a social role for 

performing the activity, the entrepreneur. Research 

therefore focuses on the performers of 

the activity and the framework of conditions affecting 

their performance. 

To understand why some people, and not other 

people, become entrepreneurs, we compare 

entrepreneurs to non-entrepreneurs (Schøtt 

2003). Specifically, we examine how education 

and training affect people’s choice of vocation. 

To understand why Denmark has its level of activity 

of entrepreneurship, we compare Denmark 

to other societies, that is, we compare 

their activities and we compare their frameworks, 

in order to see how the activity is shaped 

by its framework. 

Individuals are compared to one another, and 

societies are compared to one another. This requires 

comparable data on individuals and 

comparable data on societies. 

Global Entrepreneurship Monitor surveys 

Data on societies and people are gathered in 

the research program Global Entrepreneurship 

Monitor conducted by the Global Entrepreneurship 

Research Association, which maintains an 

informative homepage www.gemconsortium. 

org (GERA 2007; Reynolds et al., 2005). GEM 

has so far been in full operation for 10 years, 

1999 through 2008, and a total of 66 countries 

or societies have participated. Denmark has 

participated in each of the 10 years, but most 

countries have participated in only some of the 

years. 

The 66 participating countries or societies include 

39 developed societies: Australia, Austria, 

Belgium, Bosnia, Canada, Croatia, the Czech Republic, 

Denmark, Finland, France, Germany, 

Greece, Hong Kong, Hungary, Iceland, Ireland, 

Israel, Italy, Japan, Latvia, Macedonia, the Netherlands, 

New Zealand, Norway, Poland, Portugal, 

Puerto Rico, Romania, Russia, South Korea, 

Serbia, Singapore, Slovenia, Spain, Sweden, 

Switzerland, Taiwan, the United Kingdom and 

the United States. The survey has also been conducted 

in 27 developing countries: Angola, Argentina, 

Bolivia, Brazil, Chile, China, Colombia, 

Dominican Republic, Ecuador, Egypt, India, Indonesia, 

Iran, Jamaica, Jordan, Kazakhstan, Mexico, 

Myanmar, Peru, Philippines, South Africa, 

Thailand, Turkey, Uganda, United Arab Emi rates, 

Uruguay and Venezuela. Results are robust with 

respect to a slightly different classification of developed 

versus developing countries. 

The performers of entrepreneurial activities are 

identified and investigated in a survey of the 

adult population. In Denmark we have in each 

of the 10 years from 1999 to 2008 conducted a 

national probability sample survey of the adult 

population, with a new sample each year, pooling 

them for a total of 27.125 respondents interviewed 

on the telephone. Weighting the respondents 

enhances the validity of these surveys 

(weights, based on gender, age and region, 

are used mainly in the analyses in Chapters 3). 

The questionnaire asks the adults about their 

involvement in entrepreneurship, so as to identify 

entrepreneurs (used in Chapter 3), and 

about their education and training for entrepreneurship 

(used mainly in Chapters 6 to 12). Similar 

surveys have been conducted around the 

world, asking the same questions everywhere, 

and pooled into a total of about one million respondents 

interviewed in 1999 to 2008 in the 66 

countries. 

The conditions affecting performance of the activity 

have been measured, in 2008 and preced- 

16

ing years, in Denmark and in the other countries 

participating in GEM that year. The framework 

conditions in a country are rated by a panel of 

experts usually comprising at least 36 experts 

in the country. The panel rates several conditions, 

so each condition is scored. Conditions 

have been assessed in Denmark in 2008 and 

preceding years, and likewise in the other countries 

participating in GEM. Thereby we create a 

time series for each framework condition in 

Denmark so as to track changes (Chapter 4), to 

understand dynamics in the country, and to offer 

leading indicators for policy-making. We 

also compare conditions in Denmark to conditions 

in other countries in order to estimate 

effects of conditions upon entrepreneurship 

(Chapter 5), and also to offer comparative upto-date 

leading indicators for policy-making. 

The coupling between entrepreneurship and its 

conditions, especially policies, differs between 

developed countries and developing countries 

(Schøtt 2007c). In a typical developed society, 

there is a tight coupling between entrepreneurship 

and policy, whereas the coupling is far 

looser in a typical developing society (Schøtt 

and Jensen 2009). Therefore, it is often most 

informative to examine the two kinds of societies 

separately, and to compare Denmark to the 

other developed societies. 

Registry data 

Entrepreneurship can also be investigated 

through data from registries. Registries are 

compiled by Statistics Denmark on persons, 

firms and people’s work in firms (Danmarks 

Statistik 2005). The registry data on persons 

cover the lives of people. The Danish registries 

of individuals are very rich and unique, better 

than registries in most other countries, so they 

enable original and detailed analyses of, for example, 

the processes leading into the entrepreneurial 

vocation, out of it, and switches (Erhvervs- 

og Boligstyrelsen, 2002). The national 

registries of individuals are rather idiosyncratic 

to each country, and have apparently not been 

used for individual-level comparisons among 

people in different nations. 

of firms. The Danish registries of firms are also 

better than registries in most other countries, 

so they allow analyses of the growth of firms in 

Denmark (Erhvervs- og Byggestyrelsen, 2008). 

The national registries of firms are rather idiosyncratic 

to each country, but are nevertheless 

used for comparisons among countries (Eurostat 

2005; Hoffmann et al, 2005; OECD 2008). 

Registry-based measures of entrepreneurial 

activity are fairly highly correlated with our 

TEArate (Schøtt, 2005b). But, of course, the differences 

among countries in registration reduces 

validity of the comparisons among countries. 

Our adult population survey has some distinct 

advantages over registry data. First, our survey 

is up-to-date and provides up-to-date leading 

indicators on entrepreneurship. Second, our 

survey goes beyond the behavior and investigates, 

for example, people’s intentions to become 

entrepreneurs, their motivations, and 

their expectations. Third, our survey is conducted 

around the world, using the same method 

everywhere, so as to enable valid comparisons. 

Having now reviewed our methods, we can embark 

on our analyses of entrepreneurship in society. 

The registry data on firms cover the evolution 

17

Part 2 

society 

Chapter 3 

National entrepreneurial activity: 

Trends in Denmark and comparisons to other societies 


Denmark? More specifically, this chapter addresses 

the issues, Is the trend upward, stable 

or downward? How does the level in Denmark 

compare to the levels in other societies? 

To investigate entrepreneurial activity, we identify 

the entrepreneurs in the adult population 

by our GEM-survey (Chapter 2; Reynolds et al., 

2005; Schøtt 2006a:20-23; 2007a:24-28; 2008:17- 

19) 

Entrepreneurial activity is not a single circumscribed 

activity but is often seen as an activity 

with a lifecycle that unfolds in phases as rather 

distinct activities (Schøtt 2006a:16-17, 56-63; 

2007a:22-24, 29-31; 2008:17). We distinguish six 

specialized activities: 

– prospecting or intending to start a new business 

in the foreseeable future. 

– starting a business, actively, such as by 

looking for facilities and financing. 

– new business operation, paying salary or 

compensation, but not yet for long. 

– established business operation, paying salary 

or compensation for long. 

– discontinuing or closing the business. 

– investing in a business. 

The last activity, though, is not part of the lifecycle 

of entrepreneurship. 

These activities are performed by the entrepreneur. 

The six specialized activities entails a typology 

of six specialized entrepreneurs: 

– prospective starter 

– starter 

– owner-manager of a new business 

– owner-manager of an established business 

– ex-owner-manager 

– investor 

These specialized roles tend to be sequential, 

but may also overlap. 

The prospective starters are those who answer 

Yes to the following question, 

Are you, alone or with others, expecting to start 

a new business, including any type of self-employment, 

within the next three years? 

The starters are those answering Yes to either 

of the following two questions, 

Are you, alone or with others, currently trying to 

start a new business, including any self-employment 

or selling any goods or services to others? 

Are you, alone or with others, currently trying to 

start a new business or a new venture for your 

employer – an effort that is part of your normal 

work? 

A starter, furthermore, is also required to report 

to be actively starting and to be an owner of the 

startup. 

The owner-managers are those answering Yes 

to the following question, 

Are you, alone or with others, currently the owner 

of a company you help manage, self-employed, 

or selling any goods or services to others? 

Owner-managers are also asked when salary or 

compensation was first paid to owners so as to 

distinguish between new and established businesses. 

The cutoff is set at 3½ years. 

19

The ex-owner-managers are those answering 

Yes to the following question, 

Have you, in the past twelve months, shut down, 

discontinued or quit a business you owned and 

managed, any form of self-employed, or selling 

goods or services to anyone (not counting a 

busines that was sold)? 

The investors are those answering Yes to the 

following question, 

Have you, in the past three years, personally 

provided funds for a new business started by 

someone else (excluding any purchases of 

stocks or mutual funds)? 

A person may of course play more than one of 

the above six specialized roles. 

Here we focus on the early phases of entrepreneurship. 

We ignore discontinuations and investing 

(Schøtt, 2008). 

The level of each entrepreneurial activity in 

Denmark in a year can be estimated by the 

prevalence or rate of the so-identified entrepreneurs 

in our surveyed sample of the adult population, 

Table 3.1 (observations are weighted, 

thereby enhancing validity). An entrepreneur 

may of course be more than one specialized 

kind of entrepreneur, and thus be included more 

than once in the counts, so the total rate of entrepreneurship 

of several kinds is somewhat 

less than the sum of the rates of specialized 

kinds of entrepreneurship. 

The rate of prospective starters has apparently 

been declining in recent years, which is quite 

surprising considering the considerable efforts 

to promote interest in entrepreneurship. 

The rate of starters actively trying to start has 

apparently been rather stable over recent years 

with some fluctuations; that it has not increased 

is also surprising considering the support. 

The rate of operating new businesses has apparently 

tended to increase in recent years, 

which is consistent with our earlier findings, 

also our counts from registration of new businesses 

(Schøtt, 2006:62). 

An overall measure of entrepreneurial activity 

in the early phase is the so-called TEA-rate (Total 

Entrepreneurial Activity, albeit this is less 

than the totality) which is the rate of starters 

and new-business owner-managers (and thus a 

bit less than the sum of their two rates). This 

TEArate correlates highly with other measures 

of entrepreneurial activity (Schøtt 2005b). The 

TEA-rate shows that entrepreneurship in the 

early phase has increased slightly in recent 

years, except perhaps in 2008. 

Another measure of entrepreneurial activity is 

the upstart-rate, the rate of new-business owner-managers 

relative to all owner-managers, 

i.e. the percentage of owner-managers who are 

new. This rate is similar to the measure of entrepreneurial 

activity based on registries, namely 

20 

Table 3.1 

Rate of adults’ participation in entrepreneurial activities. Denmark. Annually 2002-2008 

2002 2003 2004 2005 2006 2007 2008 

Prospective starters Percent of adults 9.0% 7.3% 9.9% 9.0% 8.2% 7.7% 7.4 % 

Starters Percent of adults 3.6% 3.0% 2.5% 2.4% 2.9% 2.3% 2.3 % 

New-business 

owner-managers Percent of adults 3.1% 3.3% 2.8% 2.4% 2.8% 3.1% 2.3 % 

Starters and 

new-business 

owner-managers Percent of adults (TEA) 6.5% 5.9% 5.3% 4.8% 5.3% 5.4% 4.4 % 

New-business 

Percent of ownerowner-managers 

managers 36% 37% 36% 34% 34% 34% 34 %

the percentage of firms that are newly founded 

(Erhvervs- og Byggestyrelsen 2008). The upstart-rate 

seems to have been stable in recent 

years. 

The rate of starters and new-business ownermanagers 

in the population may thus be increasing 

(though perhaps not in 2008). But this 

is a past event, even starting a business is the 

result of efforts in the past. The present and especially 

the future is probably better indicated 

by the rate of prospective starting, and this rate 

is declining. This indicator of people’s intentions 

is a leading indicator, it is ahead of the actual 

event of starting new businesses, and this 

leading indicator entails more gloomy predictions. 

Where does this bring Denmark in comparison 

to other developed countries? The commonly 

used measure for comparisons is the TEA-rate, 

Table 3.2. Entrepreneurial activity in Denmark is 

at a level that is less than half of the levels in the 

USA, Australia, New Zealand and several other 

developed societies. Denmark is below the middle 

among the developed countries, with 34% 

of the other developed countries below Denmark. 

Denmark has remained somewhere 

around the middle among the developed countries 

for quite many years (Hancock et al, 2001, 

2002, 2003, 2004; Schøtt, 2006a, 2007, 2008; 

Warhuus 2000). 

According to the upstart-rate, Denmark is also 

far below New Zealand and the United States. 

Denmark is a little below the middle, with 45% 

of the other developed countries below Denmark. 

The gloomy forecast appears at first sight to 

contradict the rosier picture painted by the recent 

official report Iværksætterindeks 2008 (Erhvervs- 

og Byggestyrelsen, 2008) and also the 

recent review of entrepreneurship policies conducted 

by the OECD (OECD 2008). However, the 

official report and the review are based mainly 

on trends some years ago in numbers of registrations 

of businesses, and are thus most similar 

to our rates of new-business owner-managers 

and both their counts of registrations and 

Table 3.2 

Rates of entrepreneurship in developed societies 

Countries with highest rates, countries with lowest rates, USA and Denmark 2002-08, 

weighted toward 2008 

Prospective Starters New-business Starters and new-business New-business 

starters owner-managers owner-managers (TEA) owner-managers 

Percent of adults Percent of adults Percent of adults Percent of adults Percent of owner-managers 

46% Macedonia 9% New Zealand 8% New Zealand 15% New Zealand 56% Russia 

35% Serbia 8% Iceland 8% Macedonia 14% Macedonia 44% Singapore 

32% Bosnia 7% Macedonia 7% S.Korea 12% Australia 44% New Zealand 

… … … … … 

14% USA 7% USA 4% USA 11% USA 42% USA 

68 percentile rank 89 percentile rank 87 percentile rank 89 percentile rank 89 percentile rank 

… … … … … 

8% Denmark 3% Denmark 3% Denmark 5% Denmark 34% Denmark 

26 percentile rank 26 percentile rank 53 percentile rank 34 percentile rank 45 percentile rank 

… … … … … 

6% Russia 2% Japan 1% Romania 3% Austria 23% Austria 

6% Netherlands 2% Puerto Rico 1% France 3% Belgium 20% Japan 

4% Japan 1% Taiwan 1% Belgium 3% Puerto Rico 19% Greece 

21

our survey counting new-business owner-managers 

as a population rate are showing an increase 

in recent years (except 2008). The old 

trends shown in the official report and the review 

thus reconfirm the trends that were already 

revealed and documented by our GEMsurveys 

in recent years (Schøtt, 2006a, 2007, 

2008). However, going beyond the counts based 

on historical registries, our GEM surveys offer 

more up-to-date indicators and even a leading 

indicator which can better reveal current changes, 

and these look gloomier. 

The level of early-phase entrepreneurial activity, 

as a rate or prevalence in the adult population 

in Denmark, is today a little less than the 

typical for developed countries. Indeed, for several 

years, Denmark has been around the middle 

among the developed countries. 

22

Chapter 4 

National conditions for entrepreneurship: trends in Denmark and 

comparisons to other societies 

How are the national conditions for entrepreneurship 

in Denmark? More specifically, this 

chapter answers the questions, Are cultural and 

institutional conditions in Denmark improving 

or deteriorating? How does the framework in 

Denmark compare to the framework in other societies? 

Entrepreneurship does not exist in a vacuum. 

Entrepreneurship is an activity that is organized 

in society and is shaped by conditions prevailing 

in the society. Entrepreneurial activity in society 

flourishes under some conditions and 

vanishes under other conditions (Morrison 

2000; Shane 1992, 1993). The conditions in society 

affecting entrepreneurship are denoted 

the framework conditions in the society. The 

framework conditions are subject to intervention 

by the authorities in society, indeed, in recent 

decades they have become a focus for 

policy-making (Schøtt and Jensen 2008). Some 

framework conditions are in the culture of society, 

notably in its values and knowledge. Other 

framework conditions are in the social institutions 

of society, notably in its supportive arrangements, 

mobilization and allocation of resources, 

and regulation of the market. 

The framework conditions in society are numerous 

and only partly discerned, and their effects 

are even less known. Here we examine 14 

framework conditions in society, namely 4 cultural 

conditions and 10 institutional conditions, 

Figure 4.1. 

These conditions are measured in each country 

participating in GEM. A national panel of experts 

on entrepreneurship in the country is surveyed 

and assesses each condition on a scale 

from a low of 1 to a high of 5 (Chapter 2; Schøtt, 

2006a:20-21, 64-95; 2007:33-49; 2008:21-32). 

Annual assessments of conditions in Denmark 

and other countries enable us to track changes 

over time in Denmark and to compare Denmark 

to other countries. The up-to-date measures of 

framework conditions, in so far as they affect 

future entrepreneurship, are actually leading indicators 

of entrepreneurial activity. 

Figure 4.1 

Framework of culture and institutions in society 

CULTURE 

Education in entrepreneurship during schooling 

Skills in entrepreneurship in the population 

Individualism as a value in culture 

Esteem of the vocation of entrepreneur 

ENTREPRENEURSHIP 

INSTITUTIONS 

Financial resources for entrepreneurship 

Government policies for entrepreneurship 

Public programs for entrepreneurship 

Technology transfer to entrepreneurship 

Commercial and legal infrastructure 

Internal market openness to new firms 

Physical infrastructure for new firms 

Intellectual property rights 

Support for growth-entrepreneurship 

Opportunities for business 

23

In this chapter, we shall examine each condition, 

track its changes in Denmark and compare 

Denmark to other developed countries, and 

then, in the next chapter, we shall estimate effects 

of the conditions upon entrepreneurial activity. 

The extent of entrepreneurial education in Denmark 

has changed, Table 4.1. The conveyance 

of entrepreneurial knowledge and skills has 

tended to increase over the years (for specific 

educational initiatives, see Erhvervs- og Byggestyrelsen 

2008: 69-71). 

Education in entrepreneurship 

Education here refers to the formal instruction, 

all the way up through higher education, that 

provides knowledge and skills for performing 

the entrepreneurial role (Schøtt, 2006a:68, 

2007a:35, 2008:29). This kind of education in 

entrepreneurship in Denmark is assessed annually 

by asking a panel of experts to ascertain 

truthfulness of each of the following statements, 

– In Denmark, teaching in primary and secondary 

education encourages creativity, selfsufficiency, 

and personal initiative. 


education provides adequate instruction 

in market economic principles. 


education provides adequate attention 

to entrepreneurship and new firm creation. 

– In Denmark, colleges and universities provide 

good and adequate preparation for starting 

up and growing new firms. 

– In Denmark, the level of business and management 

education provide good and adequate 

preparation for starting up and growing 

new firms. 

– In Denmark, the vocational, professional, 

and continuing education systems provide 

good and adequate preparation for starting 

up and growing new firms. 

Each expert ascertains the truthfulness of each 

statement in terms of ’Completely false’, ’Somewhat 

false’, ’Neither true nor false’, ’Somewhat 

true’ and ’Completely true’. The assessment is 

quantified on a scale from 1 to 5. The assessments 

are then averaged across the experts 

and averaged across the six statements for a 

measure for the year. With measurement taken 

in 2008 and preceding years we can track recent 

changes in the extent of education in entrepreneurship. 

Table 4.1 

Education for entrepreneurship 

in Denmark 

Annually from 2002 to 2008 

2002 2003 2004 2005 2006 2007 2008 

2,3 2,2 2,3 2,3 2,4 2,5 2,4 

Where does this entrepreneurial education 

bring Denmark in comparison to other countries? 

The entrepreneurial content in education 

is likewise assessed in the other countries participating 

in GEM. In the seven years from 2002 

to 2008, the framework conditions have been 

assessed in 39 other developed countries listed 

in Chapter 2. A summary measure that reduces 

year-to-year fluctuations as well as emphasizes 

the most recent time is a weighted average, 

weighing the second year (2003) of measurement 

twice as much as the first year (2002), the 

third year (2004) three times as much as the 

first year, and so on up to the most recent year 

(2008), that is weighted seven times as much as 

the first year. This weighted average is used for 

comparing the framework conditions among 

the developed countries, Table 4.2 (and similar 

tables in the rest of this chapter). 

Entrepreneurial education is more extensive in 

several developed countries, notably Singapore, 

USA and Latvia. Among the developed 

countries, Denmark is far below the middle, 

71% of the others are above and only 29% of the 

others are below. 

In short, although the entrepreneurial content 

in education in Denmark has been expanding, it 

is still far less than typical for the developed 

countries. 

24

Table 4.2 

Education in entrepreneurship 

The three countries with the most 

education, the three countries with 

the least, and Denmark 

2002-08, weighted toward 2008 

Singapore 3.0 

USA 2.9 

Latvia 2.8 

… 

Denmark 2.4 29 percentile rank 

… 

Greece 2.2 

Portugal 2.0 

Japan 1.8 

Skills for the entrepreneurial vocation 

Skills refer to the skills of the entrepreneurial 

vocation, which prevail in the population, and 

encompass techniques for starting and organizing 

a firm, understanding markets and managing 

growth (Schøtt, 2006a:70, 2007a:36, 2008:30; 

Schøtt and Ottósson 2009). Skills are acquired 

not only through formal education, but also 

through experience, training and networking 

(Bager and Nielsen, 2009; Schøtt 2006b, 2007e, 

2008a, 2008b; Schøtt and Christensen 2005; 

Schøtt and Klyver 2006). More over, skills are 

carried from existing firms when entrepreneurs 

move to start new firms (Schøtt 2005d, Schøtt 

and Jensen 2007). The skills of the population 

in Denmark are assessed annually by asking a 

panel of experts to ascertain the truthfulness of 

each of the following statements, 

– In Denmark, many people know how to start 

and manage a high-growth business. 

– In Denmark, many people know how to start 

and manage a small business. 

– In Denmark, many people have experience 

in starting a new business. 

– In Denmark, many people can react quickly 

to good opportunities for a new business. 

– In Denmark, many people have the ability to 

organize the resources required for a new 

business. 






are then averaged across the experts and 

averaged across the five statements for a measure 

for the year. With measurement taken in 2008 

and preceding years, we can track recent changes 

in entrepreneurial skills in the population. 

The skills in the population in Denmark have 

changed, Table 4.3. The skills have tended to increase 

considerably over time. 

Table 4.3 




2002 2003 2004 2005 2006 2007 2008 

2.0 2.2 2.3 2.6 2.6 2.8 2.7 

Where does this level of skills bring Denmark in 

comparison to other countries? Skills are assessed 

likewise in other countries. Skills are 

more extensive in many societies, notably in 

Iceland, Hong Kong and USA, Table 4.4. Among 

the developed countries, Denmark is slightly 

above the middle, with 45% of the others above 

and 55% of the others below. 

Table 4.4 



skills, the three countries with the 

least, and Denmark 


Iceland 3.5 

Hong Kong 3.4 

USA 3.2 

… 


… 

Sweden 1.9 

France 1.8 

Japan 1.7 

25

In short, although entrepreneurial skills have increased 

in the population in Denmark, they are 

only a little above the typical for the developed 

countries. 


in 2008 and in preceding years, we can track recent 

changes in the value attached to individualism. 

Individualism as a value in culture 

Values refer to what is appreciated in society, 

what is considered good (Schøtt, 2006a:64, 

2007a:33, 2008:28). Our culture of modernity 

values the individual as an actor that is not 

only capable of taking action, and acting alone, 

by own effort, but also taking responsibility 

for acting alone and finding ways to act and to 

gain by acting (Weber, 1930). Modernity thus 

has a value that we briefly can call individualism, 

in contrast to collectivism, valuing the collectivity 

and the collective good (Mueller and 

Thomas 2000; Nakata and Sivakumar 1996; 

Thomas and Mueller 2000; Tiessen 1997). Denmark 

is in many ways a highly modern society 

but is actually also rather collectively oriented. 

The value attached to individualism in Denmark 

is assessed annually by asking a panel of experts 

to ascertain the truthfulness of each of the 

following statements, 

– In Denmark, the national culture is highly 

supportive of individual success achieved 

through own personal efforts. 

– In Denmark, the national culture emphasizes 

self-sufficiency, autonomy, and personal initiative. 

– In Denmark, the national culture encourages 

entrepreneurial risk-taking. 

– In Denmark, the national culture encourages 

creativity and innovativeness. 

– In Denmark, the national culture emphasizes 

the responsibility that the individual (rather 

than the collective) has in managing his or her 

own life. 







and averaged across the five statements for a 

The value attached to individualism in Denmark 

has changed, Table 4.5. Individualism has 

tended to become more highly valued over 

time. 

Table 4.5 

Cultural value of individualism 



2002 2003 2004 2005 2006 2007 2008 

2.0 2.4 2.4 2.5 2.6 2.9 2.7 

How does the valued attached to individualism 

in Denmark compare to its value in other countries? 

Individualism is assessed likewise in other 

countries. Several societies are highly individualistic, 

notably USA, Taiwan and Israel, Table 

4.6. Among the developed countries, Denmark 

is not highly individualistic, 58% of the 

others are more individualistic, and 42% of the 

others are less individualistic. 

Table 4.6 

Cultural value of individualism 


individualistic values, the three 

countries with the least 

individualistic values, and Denmark 


USA 4.2 

Taiwan 4.1 

Israel 4.0 

… 


… 

France 2.0 

Portugal 1.9 

Sweden 1.8 

26

In short, there has been an increase in the cultural 

value attached to individualism in Denmark 

over time. Denmark is almost as individualistic 

as is typical among the developed countries. 

Cultural esteem of the entrepreneurial 

vocation 

Esteem refers to the cultural prestige of the vocation 

or role of the entrepreneur among the 

vocations in society, as this prestige motivates 

people to pursue this vocation rather than other 

occupations (Schøtt, 2006a:66, 2007a:34, 

2008:28-29). Esteem of the entrepreneurial vocation 

in Denmark is assessed annually by asking 

a panel of experts to ascertain the truthfulness 

of each of the following statements, 

– In Denmark, the creation of new ventures is 

considered an appropriate way to become 

rich. 

– In Denmark, most people consider becoming 

an entrepreneur as a desirable career 

choice. 

– In Denmark, successful entrepreneurs have 

a high level of status and respect. 

– In Denmark, you will often see stories in 

the public media about successful entre preneurs. 

– In Denmark, most people think of entrepreneurs 

as competent, resourceful individuals. 









in 2008 and preceding years, we can track 

changes in the cultural esteem of the entrepreneurial 

vocation. 

Table 4.7 

Esteem in Denmark 


2002 2003 2004 2005 2006 2007 2008 

2.9 2.9 3.1 3.2 3.4 3.5 3.4 

The esteem of the entrepreneurial vocation in 

Denmark has been changing, Table 4.7. The esteem 

has increased considerably over time. 

Where does this esteem bring Denmark in comparison 

to other countries? Esteem is assessed 

similarly in the other countries participating 

in GEM. Esteem of the entrepreneurial vocation 

is much higher in many other developed societies, 

notably in Taiwan, Israel and USA, Table 

4.8. Among the developed countries, Denmark 

is slightly below the middle, 53% of the 

others are above and 47% of the others are below. 

Table 4.8 

Esteem accorded the entrepreneurial 

vocation. The three countries with the 

highest esteem, the three countries 

with the lowest, and Denmark 


Taiwan 4.4 

Israel 4.4 

USA 4.1 

… 


… 

Japan 2.8 

Czech Republic 2.7 

Sweden 2.5 

In short, although the esteem of the entrepreneurial 

vocation has been increasing much in 

Denmark, Denmark remains typical among the 

developed countries. 

Institutional conditions: Financial resources 

for entrepreneurship 

Financial resources refer to the availability of 

funding for entrepreneurship (Schøtt, 2006:76, 

2007:39, 2008:22). Each year, in Denmark, the 

financial resources are assessed by asking a 

panel of experts to ascertain the truthfulness of 

each of the following statements, 

– In Denmark, there is sufficient equity funding 

available for new and growing firms 

27

– In Denmark, there is sufficient debt funding 

available for new and growing firms 

– In Denmark, there are sufficient government 

subsidies available for new and growing 

firms 

– In Denmark, there is sufficient funding available 

from private individuals (other than 

founders) for new and growing firms 

– In Denmark, there is sufficient venture capitalist 

funding available for new and growing 

firms 

– In Denmark, there is sufficient funding available 

through initial public offerings (IPOs) for 

new and growing firms 







and averaged across the six statements for a 


in 2008 and preceding years we can track recent 

changes in financial resources for entrepreneurship. 

The availability of funding in Denmark has 

changed over the years, Table 4.9. Resources 

have tended to increase during the most recent 

years. The measurement in 2008, though, was 

taken before the current crisis hit. 

Table 4.9 

Financial resources in Denmark 


2002 2003 2004 2005 2006 2007 2008 

3.2 3.0 2.5 2.6 2.7 2.9 2.8 

Where does this availability of resources for entrepreneurship 

bring Denmark compared to 

other countries? Availability is also measured 

in the other countries participating in GEM, Table 

4.10. Among the developed countries, USA, 

Israel and Taiwan have greatest availability. 

Denmark is slightly above the middle, with 55% 

of the others below. 

Table 4.10 

Financial resources 

The three countries with most financial 

resources, the three countries with 

least financial resources, and Denmark 


USA 3.6 

Israel 3.6 

Taiwan 3.5 

… 


… 

Portugal 2.3 

Bosnia 2.2 

Poland 1.8 

In short, availability of resources has been on 

the increase in recent years. Denmark is slightly 

above the middle among the developed countries. 

Government policies toward 

entrepreneurship 

Government policies toward entrepreneurship 

refer to the policies that the national government 

and more local public authorities decide 

and implement (Schøtt, 2006a:78, 2007a:40, 

2008:23). Government policies in Denmark are 

measured annually by asking a panel of experts 

to ascertain the truthfulness of each of the following 

statements, 

– In Denmark, Government policies (e.g., 

public procurement) consistently favor new 

firms. 

– In Denmark, the support for new and growing 

firms is a high priority for policy at the national 

government level. 

– In Denmark, the support for new and growing 

firms is a high priority for policy at the local 

government level. 

– In Denmark, new firms can get most of the 

required permits and licenses in about a 

week. 

– In Denmark, the amount of taxes is NOT a 

burden for new and growing firms. 

– In Denmark, taxes and other government 

regulations are applied to new and grow- 

28

ing firms in a predictable and consistent 

way. 

– In Denmark, coping with government bureaucracy, 

regulations, and licensing requirements 

it is not unduly difficult for new and 

growing firms. 

Goverment policies in Denmark have been 

changing, Table 4.11. Over time, policies have 

become more favorable. 

Table 4.11 

Government policies in Denmark 


2002 2003 2004 2005 2006 2007 2008 

2.6 2.7 2.8 3.1 3.1 3.2 3.1 

Where do these favorable policies bring Denmark 

compared to other countries? Government 

policies have been measured also in the 

other countries participating in GEM, Table 4.12. 

Among the developed countries, government 

policies are more favorable especially in Singapore, 

Iceland and Finland. Denmark is near the 

top, with 84% of the other countries having less 

favorable policies. 

Table 4.12 

Government policies 


favorable government policies, the three 

countries with the least supportive 

policies, and Denmark and USA 




Finland 3.3 

… 


USA 3.0 

… 

Sweden 2.0 

Hungary 1.8 

Bosnia 1.8 

In short, government policies in Denmark have 

become more supportive, and Denmark is 

among the countries with most favorable policies. 


Public programs refer to the programs that are 

publicly available, and mostly supported by 

funds from the public, channelled through national 

and more local administrations (Schøtt, 

2006a:80, 2007a:41, 2008:23-24). The adaquacy 

of the public programs in Denmark are assessed 

annually by asking a panel of experts to ascertain 


– In Denmark, a wide range of government 

assistance for new and growing firms can be 

obtained through contact with a single agency. 

– In Denmark, science parks and business incubators 

provide effective support for new 

and growing firms. 

– In Denmark, there are an adequate number 

of government programs for new and growing 

businesses. 

– In Denmark, the people working for government 

agencies are competent and effective in 

supporting new and growing firms. 

– In Denmark, almost anyone who needs 

help from a government program for a new 

or growing business can find what they 

need. 

– In Denmark, government programs aimed at 

supporting new and growing firms are effective. 

Adequacy of public programs in Denmark have 

been changing, Table 4.13. Adequacy of the 

public programs has apparently been fairly constant. 

Table 4.13 




2002 2003 2004 2005 2006 2007 2008 

3.0 2.9 2.9 3.1 3.0 3.1 3.0 

29

Where is Denmark positioned compared to other 

countries in terms of supportive public programs? 

Public programs are likewise assessed 

in other countries, Table 4.14. Among the developed 

countries, public programs are more supportive 

in some countries, notably Austria, Germany 

and Ireland. Denmark has quite supportive 

programs, only 20% of the other countries 

have more supportive programs, whereas 80% 

of the others have less supportive programs. 

That Denmark has fairly supportive public programs 

is hardly surprising, as Denmark is still a 

welfare society, with welfare extendig to public 

support for private business, especially under 

the current right-wing government. 

Table 4.14 



supportive public programs, the 

three countries with the least, and 

Denmark and the USA 


Austria 3.5 

Germany 3.4 

Ireland 3.3 

… 


USA 3.0 

… 

Russia 2.2 

Latvia 2.1 

Bosnia 1.9 


– In Denmark, new technology, science, and 

other knowledge are efficiently transferred from 

universities and public research centers to new 

and growing firms. 

– In Denmark, new and growing firms have just 

as much access to new research and technology 

as large, established firms. 

– In Denmark, new and growing firms can afford 

the latest technology. 

– In Denmark, there are adequate government 

subsidies for new and growing firms to acquire 

new technology. 

– In Denmark, the science and technology base 

efficiently supports the creation of world-class 

new technology-based ventures in at least one 

area. 

– In Denmark, there is good support available 

for engineers and scientists to have their ideas 

commercialized through new and growing 

firms. 

Technology transfer in Denmark has been 

changing over the years, Table 4.15. Technology 

transfer has increased over time. 

Table 4.15 

Technology transfer in Denmark. 


2002 2003 2004 2005 2006 2007 2008 

2.3 2.5 2.4 2.5 2.6 2.6 2.7 

In short, whereas the supportiveness of public 

programs in Denmark has been rather stable, 

Denmark ranks very high compared to other developed 

countries. 

Technology transfer to entrepreneurship 

Technology transfer denotes the movement of 

technological knowledge from public research 

institutions to entrepreneurship (Schøtt, 

2006a:82, 2007a:42, 2008:24). The extent of 

technology transfer in Denmark is assessed annually 

by asking a panel of experts to ascertain 

Where does the increase bring Denmark, in 

comparison to other countries? Technology 

transfer is measured likewise in many other 

countries, Table 4.16. Among the developed 

countries, technology transfer is especially extensive 

in Taiwan, Switzerland and the USA. 

Denmark is a little above the middle, with 61% 

of the other countries below Denmark in technology 

transfer. 

In short, technology transfer has been increasing 

in Denmark, and Denmark is a little above 

the middle among the developed countries. 

30

Table 4.16 

Technology transfer 

The three countries with most 

technology transfer, the three 

countries with least, and Denmark 


Taiwan 3.4 

Switzerland 3.2 

USA 3.1 

… 


… 

Macedonia 2.0 


Bosnia 1.8 

Table 4.17 




2002 2003 2004 2005 2006 2007 2008 

3.4 3.4 3.4 3.5 3.7 3.5 3.3 

How does Denmark compare to other countries? 

The infrastructure is also measured in the 

other countries participating in GEM, Table 4.18. 

The availability is higher in several countries, 

notably in USA, Israel and Belgium. Among the 

developed countries, Denmark is well above the 

middle, with 68% of the others below. 

Commercial and legal infrastructure for 

entrepreneurship 

Commercial and legal infrastructure refers to 

the availability and affordability of high quality 

services of commercial, legal and professional 

kinds (Schøtt, 2006a:84, 2007a:43, 2008:24-25). 

The infrastructure in Denmark is measured annually 

by asking a panel of exerts to ascertain 

the truthfulness of each of the following statements, 

– In Denmark, there are enough subcontractors, 

suppliers, and consultants to support 

new and growing firms. 


the cost of using subcontractors, suppliers, 

and consultants. 

– In Denmark, it is easy for new and growing 

firms to get good subcontractors, suppliers, 

and consultants. 


firms to get good, professional legal and accounting 

services. 


firms to get good banking services (checking 

accounts, foreign exchange transactions, letters 

of credit, and the like). 

The infrastructure in Denmark has changed 

over the years, Table 4.17. The availability has 

tended to increase over the years, although it 

may have dropped in 2008. 

Table 4.18 


The three countries with most commercial 

and legal infrastructure, the three 

countries with least, and Denmark 


USA 3.8 

Israel 3.7 

Belgium 3.7 

… 


… 

Croatia 2.7 

Bosnia 2.7 

Japan 2.1 

In short, availability of commercial and legal 

services has tended to increase in Denmark 

over time, and Denmark is well above the middle 

among the developed countries. 

Internal market openness 

Openness of the internal market refers to the 

ease of entry into the market for new firms 

(Schøtt, 2006a:86, 2007a:44, 2008:25). The 

openness of the internal market in Denmark is 

assessed annually by asking a panel of experts 

to ascertain the truthfulness of each of the following 

statements, 

31

– In Denmark, the markets for consumer goods 

and services change dramatically from year to 

year. 

– In Denmark, the markets for business-tobusiness 

goods and services change dramatically 

from year to year. 

– In Denmark, new and growing firms can easily 

enter new markets. 

– In Denmark, the new and growing firms can 

afford the cost of market entry. 

– In Denmark, new and growing firms can enter 

markets without being unfairly blocked by 

established firms. 

– In Denmark, the anti-trust legislation is effective 

and well enforced. 

The openness in Denmark has changed over 

the years, Table 4.19. The internal market has 

tended to become a little more open over 

time. 

Table 4.20 

Internal market openness 

The three countries with the widest internal 

market openness, the three countries 

with narrowest, and USA and Denmark 


Taiwan 3.6 

S.Korea 3.5 


… 

USA 3.1 

… 


… 

Israel 2.6 

Portugal 2.6 

Spain 2.5 

Physical infrastructure for entrepreneurship 

Table 4.19 

Internal market openness in Denmark 


2002 2003 2004 2005 2006 2007 2008 

2.6 2.6 2.5 2.8 2.9 2.7 2.7 

How open is the internal market in Denmark in 

comparison to other countries? The openness 

is assessed in the other countries participating 

in GEM, Table 4.20. Many countries are far more 

open, notably Taiwan, South Korea and Iceland, 

and also the USA. Among the developed countries, 

Denmark is considerably below the middle, 

with only 39% of the other societies less 

open. 

In short, although the internal market in Denmark 

has tended to become a little more open 

over the years, it is much below the middle 

compared to other developed countries. 

Physical infrastructure encompasses facilities 

for transportation and communication, their 

availability and affordability and speed of obtaining 

them (Schøtt, 2006a:88, 2007a:45, 

2008:25-26). Physical infrastructure in Denmark 

is assessed annually by asking a panel of experts 



– In Denmark, the physical infrastructure (roads, 

utilities, communications, waste disposal) provides 

good support for new and growing 

firms. 

– In Denmark, it is not too expensive for a new 

or growing firm to get good access to communications 

(phone, Internet, etc.). 

– In Denmark, a new or growing firm can get 

good access to communications (telephone, 

internet, etc.) in about a week. 


the cost of basic utilities (gas, water, electricity, 

sewer). 

- In Denmark, new or growing firms can get 

good access to utilities (gas, water, electricity, 

sewer) in about a month. 

The physical infrastructure in Denmark has 

changed over the years, Table 4.21. The physi- 

32

cal infrastructure has tended to improve over 

the years, although perhaps not most recently, 

in 2008. 

Table 4.21 

Physical infrastructure in Denmark 


2002 2003 2004 2005 2006 2007 2008 

3.9 4.1 4.0 4.2 4.4 4.3 4.2 

How is the physical infrastructure in Denmark 

compared to other countries? The physical infrastructure 

is assessed likewise in the other 

countries particpating in GEM, Table 4.22. The 

physical infrastructure is better in some societies, 

notably Hong Kong, Singapore, Switzerland 

and the USA. Among the developed countries, 

Denmark is far above the middle, with 74% 

of the others below. 

Table 4.22 

Physical infrastructure 

The three countries with most physical 

infrastructure, the three countries with 

least, and USA and Denmark 


Hong Kong 4.7 



… 

USA 4.3 

… 


… 

Italy 3.0 

Puerto Rico 3.0 


In short, the physical infrastructure in Denmark 

has tended to improve a little over time, and 

Denmark is much above the middle among the 

developed countries. 


Intellectual property rights refer to the establishment 

of private ownership of knowledge 

(Schøtt, 2006a:90, 2007a:46, 2008:26). This 

prop erty right in Denmark is assessed annually 

by asking a panel of experts to ascertain the 


– In Denmark, the Intellectual Property Rights 

(IPR) legislation is comprehensive 

– In Denmark, the Intellectual Property Rights 

(IPR) legislation is efficiently enforced. 

– In Denmark, the illegal sales of ’pirated’ software, 

videos, CDs, and other copyrighted or 

trademarked products is not extensive. 

– In Denmark, new and growing firms can trust 

that their patents, copyrights, and trade marks 

will be respected. 

– In Denmark, it is widely recognized that inventors’ 

rights for their inventions should be 

res p e c t e d . 

The rights in Denmark have been changing over 

the years, Table 4.23. The rights have been expanding 

considerably over time, although perhaps 

not in 2008. 

Table 4.23 

Intellectual property rights in Denmark 


2002 2003 2004 2005 2006 2007 2008 

3.3 3.2 3.5 3.7 3.6 3.9 3.7 

Where do these intellectual property rights 

bring Denmark in comparison to other countries? 

Rights are assessed likewise in the other 

countries participating in GEM. Rights are more 

extensive in Switzerland, Australia and Singapore, 

Table 4.24. Denmark is among the developed 

countries with very extensive rights, as 

79% of the other countries have less comprehensive 

rights. 

33

Table 4.24 



protective intellectual property rights, 

the three countries with the least, 

and USA and Denmark 



Australia 4.2 


… 

USA 3.8 

… 


… 

Serbia 2.2 

Russia 2.1 

Bosnia 1.8 

In short, the intellectual property rights in Denmark 

have expanded, and Denmark is among 

the developed countries with the most extensive 

rights. 

what false’, ’Neither true nor false’, ’Somewhat 






in 2008 and a few preceding years we can track 

recent changes in support for growth-entrepreneurship 

(Schøtt 2008:21-22). 

Support for growth-entrepreneurship in Denmark 

has tended to increase in recent years. 

This assessment is consistent with the changes 

in entrepreneurship policy and implementations 

(Erhvervs- og Byggestyrelsen, 2007). 

Table 4.25 



Annually 2005 to 2008 

2005 2006 2007 2008 

3.4 3.3 3.7 3.6 


The institutional support for growth-entrepreneurship 

in Denmark is assessed by asking experts 



– In Denmark, there are many support initiatives 

that are specially tailored for high-growth 

entrepreneurial activity. 

– In Denmark, policy-makers are aware of the 

importance of high-growth entrepreneurial 

activity. 

– In Denmark, people working in entrepreneurship-support-initiatives 

have sufficient skills 

and competence to support high-growth 

firms. 

– In Denmark, potential for rapid growth is often 

used as a selection criterion when choosing 

recipients of entrepreneurship support. 

– In Denmark, supporting rapid firm-growth is 

a high priority in entrepreneurship policy. 


statement in terms of ’Completely false’, ’Some- 

Where does this bring Denmark compared to 

other countries? Support for growth-entrepreneurship 

is also assessed in the other countries 

participating in GEM by the same questions to a 

national panel of experts there, Table 4.26. Ire- 

Table 4.26 



support, the three countries with the 

least support, and Denmark 


Ireland 4.0 

USA 3.7 


… 


… 

Latvia 2.7 

Czech Republic 2.5 

Bosnia 2.1 

34

land, the USA and Singapore are more supportive 

of growth-entrepreneurship. Denmark is 

near the top, with 91% of the other countries below. 

In short, support for growth-entrepreneurship 

has been increasing in Denmark, and Denmark 

is among the developed countries with most 

support for growth-entrepreneurship. 

Opportunities for entrepreneurship. 

Opportunities refer to the opportunities that 

people in the society have for exploiting business 

ideas by starting, running and expanding 

businesses (Schøtt, 2006a:74, 2007b:38, 

2008:27). Opportunities in Denmark are assessed 

annually by asking a panel of experts to 

ascertain truthfulness of each of the following 

statements, 

– In Denmark, there are plenty of good opportunities 

for the creation of new firms. 

– In Denmark, there are more good opportunities 

for the creation of new firms than there 

are people able to take advantage of them. 

– In Denmark, good opportunities for new 

firms have considerably increased in the past 

five years. 

– In Denmark, individuals can easily pursue 

entrepreneurial opportunities. 

– In Denmark, there are plenty of good opportunities 

to create truly high growth firms. 

Each expert ascertains truthfulness of each 

statement in terms of ’Completely false’, 

’Somewhat false’, ’Neither true nor false’, 

’Somewhat true’ and ’Completely true’. The 

assessment is quantified on a scale from 1 to 

5. The assessments are then averaged across 

the experts and averaged across the five statements 

for a measure for the year. With measurement 

taken in 2008 and preceding years we 

can track changes in opportunities for entrepreneurship. 

Table 4.27 

Opportunities in Denmark 


2002 2003 2004 2005 2006 2007 2008 

3.3 3.3 3.3 3.5 3.6 3.6 3.4 

Where does this enlargening of opportunities 

bring Denmark now, in comparison to other 

countries? Opportunities are assessed in the 

other countries participating in GEM, Table 

4.28. Opportunities are greater in several countries, 

notably USA, Australia and Iceland. 

Among the developed countries, Denmark is 

considerably above the middle, with 74% of the 

others below. 

Table 4.28 

Opportunities for starting 

The three countries with greatest 

opportunities, the three countries with 

least, and Denmark 


USA 3.9 

Australia 3.7 


… 


… 

Hungary 2.8 

Sweden 2.8 

Italy 2.7 

In short, opportunities in Denmark have been 

enlargening over the years and Denmark is now 

far above typical, above the middle, among the 

developed countries in its opportunities for 

starting, running and expanding businesses. 

Opportunities have been changing, Table 4.27. 

Opportunities in Denmark have been enlargening 

over time, although perhaps not in 

2008. 

35

The framework overall 

The framework for entrepreneurship has now 

been examined in terms of 14 cultural and institutional 

conditions. Several of the conditions 

have changed a little over the years, and Denmark 

is more or less like other societies. 

Overall, how has the framework changed in 

Denmark? How does the framework in Denmark, 

overall, compare to the framework elsewhere? 

To examine the trend, we can average the assessments 

of the 14 conditions studied above. 

The framework in Denmark, overall, has changed 

over the years, Table 4.29. The framework has 

improved over time, perhaps except for 2008. 

Table 4.30 

Mean of national framework conditions 

Societies with the best framework, 

societies with the weakest framework, 

and Denmark 


USA 3.6 

Taiwan 3.5 


Hong Kong 3.4 


… 


… 


Japan 2.5 

Bosnia 2.4 

Table 4.29 

Mean of national framework conditions 



2002 2003 2004 2005 2006 2007 2008 

2.8 2.9 2.9 3.0 3.1 3.2 3.1 

The framework in each society can be ascertained 

by the average across the 14 conditions, 

Table 4.30. The framework, overall, is best in 

the USA, Taiwan, Singapore, Hong Kong and 

Iceland. Denmark has a framework that, overall, 

is a little better than the typical framework in 

the developed countries, with 63% of the developed 

countries having a framework less good 

than the framework in Denmark. 

Several conclusions can be drawn from the 

above analyses of framework conditions, their 

changes in Denmark, and their favorableness in 

Denmark in comparison to other developed 

countries. 

coming less favorable. On the whole, the Danish 

framework for entrepreneurship has become 

more favorable over time. 

Where does this improvement bring Denmark 

compared to other countries? Most of the Danish 

framework conditions are rather close to the 

typical situation for developed countries, and 

rank around the middle among the developed 

countries. Some conditions are much more favorable 

in Denmark than typical among the developed 

countries. Conversely, a few conditions 

are much less favorable in Denmark than typical 

among the developed countries. So, overall, 

the framework in Denmark is a little better than 

is typical for the developed countries. 

How the framework conditions affect entrepreneurship 

will be examined next. 

In recent years several of the Danish framework 

conditions have become more favorable and 

are more favorable today than some years ago. 

A few of the framework conditions seem to be 

stable, without becoming more favorable or be 

36

Chapter 5 

National dynamics: Effects of conditions upon entrepreneurial activity 


More specifically, this chapter addresses 

the questions, How are the cultural conditions 

affecting one another? How are the institutional 

conditions interrelated? How are the cultural 

and institutional conditions affecting entrepreneurship? 

The cultural and institutional conditions are a 

framework for entrepreneurship in society. A 

favorable framework enhances entrepreneurship 

whereas an unfavorable framework hampers 

performance in the society. The framework 

is favorable in some societies and less favorable 

in other societies, and this shapes the level 

of entrepreneurial activity, so the level differs 

from one society to another. 

Is entrepreneurial activity in the various countries 

tightly coupled with its framework conditions? 

Whether entrepreneurial activity goes 

hand-in-hand with a framework condition, in 

the various countries, is ascertained by the correlation 

between the activity and the condition, 

computed across the countries. Entrepreneurial 

activity in a country is measured by the TEArate 

(Chapter 3), and each of the 14 framework 

conditions were measured (Chapter 4). 

The 14 correlations between entrepreneurship 

and the 14 framework-conditions are all between 

0,0 and 0,5, for the developed countries 

(each variable is averaged across years, and 

then the correlation between variables is computed 

across the 39 developed countries). For 

the developing countries, however, entrepreneurship 

is far less coupled with its framework 

conditions (Schøtt and Jensen 2009). 

However, the framework conditions are related 

to each other. We shall exame how the institutional 

conditions are interrelated and how the 

cultural conditions are interrelated. Then we 

shall examine how the institutional conditions 

and the cultural conditions jointly affect entrepreneurship. 

Institutional conditions creating 

opportunities 

Opportunities for exploiting business ideas is a 

condition for entrepreneurship, but it is not a 

institutional condition like the eight others examined 

earlier - financial resources, government 

policies, public programs, technology 

transfer, commercial and legal infrastructure, 

internal market openness, physical infrastructure, 

and intellectual property rights. Opportunities 

should rather be considered as the condition 

emerging as a combination of the eight 

more basic institutional conditions. The institutional 

conditions are related in the way that opportunities 

can be considered a consequence 

emerging as a combination of the institutional 

conditions, not as a straightforward sum of 

these conditions, but in the way that each condition 

has a positive effect on opportunities. 

Indeed, opportunities is positively correlated 

with each of these eight basic institutional conditions, 

Table 5.1 (based on 39 developed countries; 

each correlation is statistically highly significant). 

A positive correlation of opportunities with an 

institutional condition means that, typically, a 

country with a favorable institutional condition 

Table 5.1 

Correlation of opportunities with each 

institutional condition 

Developed societies 

Financial resources 0.6 

Government policies 0.6 

Public programs 0.5 

Technology transfer 0.5 

Commercial and legal infrastructure 0.6 

Internal market openness 0.4 

Physical infrastructure 0.6 

Intellectual property rights 0.6 

Multiple correlation with all eight 0.8 

37

provides ample opportunity (and, typically, a 

country with an unfavorable condition offers 

few opportunities). Thus, specifically, a country 

with abundant financial resources typically provides 

great opportunities, (and a country with 

poor financial resources offers few opportunities, 

typically). A country with supportive policies 

typically has great opportunities (and a 

country with unsupportive policies typically offers 

few opportunities). 

The multiple correlation is the correlation of opportunities 

with an optimal combination of institutional 

conditions. Countries with a combination 

of several favorable institutional condition 

tend to have especially great opportunities, 

and this tendency is quite strong (and those 

with a combination of several unfavorable institutional 

conditions tend to have especially few 

opportunities). 

Opportunities, as a condition, is seen in Table 

5.1 to be positively correlated with each of the 

eight basic institutional conditions, and is highly 

correlated with a combination of basic institutional 

conditions (the multiple correlation is 

much higher than any of the eight correlations). 

How opportunities is the consequence of a 

combination of the basic institutional conditions 

can be ascertained by a multiple regression 

of opportunities on the conditions. In this 

regression we keep only those conditions that 

are significant. By this modeling, five of the 

eight conditions seem to matter. Internal market 

openness matters much, a country with 

wide openness typically provides many opportunities. 


also has a large effect, a country with extensive 

commercial and legal infrastructure facilitates 

opportunities. Intellectual property rights and 

government policies seem to have substantial 

effects, and financial resources appear to have 

some effect. 

These estimated effects are shown in Figure 

Figure 5.1 

Estimated effects upon opportunities from institutional conditions 


Financial 

resources 

Government 

policies 

Commercial 

and legal 

infrastructure 

Opportunities 

Internal 

market 

openness 

Intellectual 

property 

rights 

38

5.1. Statistical details are in Table 5.2 in the Appendix. 

The other institutional conditions have 

no separately discernible effects, meaning that 

the data on only 39 developed countries are insufficient 

to discern their effects, separate from 

the five discerned effects. The magnitude of an 

effect is indicated by the thickness of the arrow. 

Having examined how the institutional conditions 

are interrelated, we shall now examine effects 

among the cultural conditions. 

Cultural foundation for education and skills 

The four cultural conditions – education, skills, 

individualism, esteem – which we examined in 

Chapter 4, are likely to be related as causes and 

effects. The cultural value a society attaches to 

individualism is likely to enhance the esteem of 

the entrepreneurial vocation in the society. Education 

for the entrepreneurial vocation in the 

society is likely to be affected by the esteem of 

the vocation in the society and also by the individualism 

in society. The education, in turn, expectedly 

affects the skillfulness of the people in 

the society. This sequence of effects among 

cultural conditions will then promote entrepreneurship. 

The effects among cultural conditions 

can be estimated, like the effects among the institutional 

conditions were estimated, by regression 

analysis. 

The effect upon esteem from individualism is 

estimated to be huge (statistical details are in 

Table 5.3 in the Appendix). A society that values 

individualism also typically attaches high esteem 

to the entrepreneurial vocation. 

The effect upon education in entrepreneurship 

from individualism is also estimated to be big. 

A society that values individualism also typically 

offers much education in entrepreneurship. 

Esteem of the entrepreneurial vocation in 

society, however, has no discernible separate 

effect upon the education in entrepreneurship 

provided by the society. That an effect is not 

discerned is surprising, as a positive effect was 

expected, but perhaps this is due mainly to the 

small sample of just 39 developed countries 

(statistical details are in Table 5.4 in the Appendix). 

Skills are promoted mainly by individualism. A 

society that values individualism also typically 

has a population with extensive entrepreneurial 

skills. Education also promotes skills, also when 

controlling for individualism, but this effect is 

weaker than the effect of individualism. Esteem 

of the entrepreneurial vocation has no discernible 

separate effect upon skills of the population 

(statistical details are in Table 5.5 in the Appendix). 

These effects among the cultural conditions 

are shown in Figure 5.2, where the thickness of 

Figure 5.2 

Estimated effects among cultural conditions 


Esteem 

Education 

Individualism 

Skills 

39

an arrow represents the magnitude of the effect. 

How these cultural conditions, jointly with opportunities, 

shape entrepreneurship will be examined 

next. 

Effects upon entrepreneurship from cultural 

and institutional conditions 

The cultural conditions and the opportunities in 

a society are a framework for entrepreneurship 

in the society, and their effects upon entrepreneurial 

activity can be estimated. The level of 

entrepreneurial activity in a society is here 

measured by the TEA-index, the rate of earlyphase 

entrepreneurs in the population, as was 

examined in Chapter 3. 

First, the association of each condition with entrepreneurial 

activity is indicated by their correlation, 

Table 5.6. Each condition has a correlation 

with entrepreneurial activity that is positive. 

Entrepreneurship is even higher correlated 

with a combination of the five conditions (the 

multiple correlation is higher than any of the 

five). 

Table 5.6 

Correlation of national entrepreneurial 

activity (TEA) with opportunities and 

each cultural condition 

Developed societies (N=39) 

Opportunities 0.4 

Individualism 0.4 

Esteem 0.2 

Education 0.2 

Skills 0.3 

Multiple correlation with all five 0.5 

The effect upon entrepreneurship from each 

condition, while holding the other conditions 

constant, is estimated by a regression (statistical 

details are in Table 5.7 in the Appendix). 

Opportunities have a huge effect upon entrepreneurial 

activity. A society that provides 

abundant opportunities will also typically have 

a high level of entrepreneurial activity, whereas 

a society with few opportunities will have little 

entrepreneurial activity. Skills also appear to 

promote entrepreneurship, but the separate effect 

is much smaller than the effect of opportunities. 

Individualism, education and esteem 

have no discernible separate effects on entrepreneurial 

activity. One fourth of the variance 

among the developed countries in their entrepreneurship 

is accounted for by just these two 

conditions, opportunities and skills. Their effects 

are shown in Figure 5.3. 

Cultural conditions promote entrepreneurship 

through several sequences of positive effects. 

The cultural value attached to individualism directly 

promotes all the other three cultural conditions: 

esteem of the entrepreneurial vocation, 

the education in entrepreneurship and the entrepreneurial 

skillfulness of the population. Education 

promotes skills, so there is an indirect 

effect of education upon entrepreneurship in 

the manner that education promotes skills 

which in turn promote entrepreneurship. More 

fundamentally, although individualism does 

not directly affect entrepreneurship, it has two 

indirect effects. One indirect effect works in the 

way that individualism promotes skills which in 

turn promote entrepreneurship. Another indirect 

effect is that individualism promotes education 

which in turn promotes skills that promote 

entrepreneurship. Both indirect effects 

are positive, so individualism has a positive effect 

upon entrepreneurship. Moreover, the effects 

of individualism on education and skills 

are huge, so the effect of individualism on entrepreneurship 

is big. 

The effect of the framework upon entrepreneurship 

can be graphed by plotting the countries 

according to their entrepreneurship and to the 

optimal combination of framework conditions, 

Figure 5.4, in which an asterix marks a country. 

The association between the framework 

conditions and the level of entrepreneurship is 

obvious; a country with an extensive framework 

will also typically have a high level of entrepreneurship, 

whereas a society with a meag- 

40

Figure 5.3 

Estimated effects upon nations’ entrepreneurship from their cultural and institutional 

conditions 


Esteem 


Individualism 

Skills 

Entrepreneurship 

Opportunities 

er framework will have little entrepreneurial 

activity. 

The USA, marked U on Figure 5.4, illustrates the 

effect. The framework conditions in the USA 

are extremely extensive, and the overall framework 

in the USA is better than in any other 

country, as was measured in Chapter 4. This 

framework places the USA at the better end of 

the horizontal axis in Figure 5.4. The level of entrepreneurship 

in the USA is among the highest 

among the developed countries, this entrepreneurship 

places the USA very high up on the 

vertical axis. 

Denmark, marked D in Figure 5.4, is around the 

middle in its framework and is also around the 

middle in its entrepreneurial activity. 

In conclusion, in developed societies, both the 

cultural and the institutional framework conditions 

greatly promote entrepreneurial activity 

in discernable ways, cultural conditions enhance 

the entrepreneurial skills of the popula- 

tion and basic institutional conditions create 

opportunities for the population to bring their 

skills into entrepreneurship. 

This tight coupling between framework and entrepreneurship 

in developed societies is illustrated 

by the USA. The American framework is 

apparently better than in the other countries, 

and consequently the level of entrepreneurship 

in the USA is also among the highest among 

the developed countries. 

Denmark also displays this tight coupling between 

framework and level of entrepreneurship. 

Denmark has a level of entrepreneurship 

that is about typical for the developed countries 

(Chapter 3), and Denmark has a framework that 

is about typical (Chapter 4). This is easily understood. 

When the country has a framework 

that is about typical, then we also predict the 

resulting level of activity to be about typical. 

Having examined entrepreneurship in society, 

we now zoom in to focus on the people. 

41

Figure 5.4 

Plot of developed societies according to their combined framework conditions (horizontal 

axis) and their entrepreneurship (TEA, vertical axis) 

16 

* 

14 

* 

12 

* 

* 

* 

U 

10 

* 

8 * * * 

* * 

* 

* * 

* * 

6 * * 

* * * 

* D 

* 

* * 

* * * * 

4 * * * 

* * 

* * 

2 

3 4 5 6 7 8 9 10 11 12 

Note: 

D = Denmark 

U = USA 

42

Part 3 

people 

Chapter 6 

People’s training for entrepreneurship in Denmark and other societies 


Denmark and other societies? More specifically, 

this chapter addresses the problems, How much 

of the adult population is trained? Have people 

been trained mainly during their schooling or 

later in life? What is the organizational context 

of later training, and is this voluntary or compulsory? 

How is people’s training affected by their 

gender, age and education? Is the training expanding 

in Denmark and in other societies? 

The idea that training is affected causally by age, 

gender and education can be symbolized as a 

path diagram, in which each hypothesized causeeffect 

relationship is drawn as an arrow, Figure 

6.1. The task then is to estimate each distinct effect, 

preferably while other things are equal, and 

specifically ascertain whether the effect is different 

from zero, whether positive or negative, and 

to estimate the magnitude of the effect. 

Training is measured by asking the adult people 

in Denmark and other countries a series of 

questions, 

Have you ever taken part in training in starting a 

business at primary or secondary school? 

If yes, then the pressure was measured by 

a followup question, 

Was this voluntary or compulsory? 

Have you ever taken part in training in starting a 

business after you completed your education in 

school? 

If yes, then the context of such training 

was tapped by a series of questions, 

Have you taken this kind of training in starting a 

business as part of your formal education after 

you left or completed official schooling? 




Did you get some training in starting a business 

Figure 6.1 

Hypothesized effects upon people’s training from their gender, age and education 


Gender 

Training 

Age 

43

provided by a college or university that was not 

part of your formal education? 





organized by a local association of business 

people whose aim is to promote the welfare of 

their community’s business interests, such as a 

Chamber of Commerce or other Business Association? 





organized by a Government agency? 





organized by your past or present employer? 




Did you learn about starting a business informally? 

For example this may have been through 

reading books in your own time or observing 

other people in business, or by working in someone 

else’s business? 

Did you learn about starting a business through 

any other source of training in starting a business? 

If yes, then the provider was measured by 


What type of organization provided this 

training? 

If yes, also the pressure was measured by 

the followup question, 


Was any component of the training you took in 

starting a business conducted online? By “online”, 

I mean training that is delivered through 

the internet. 

This series of questions was posed in our adult 

survey in 2008 in Denmark and 21 other developed 

countries (namely Belgium, Bosnia, Croatia, 

Finland, France, Germany, Greece, Hungary, 

Iceland, Ireland, Israel, Italy, Japan, Latvia, 

Macedonia, Romania, Serbia, Slovenia, South 

Korea, Spain and United Kingdom), and also in 

many developing countries, which are not analyzed 

here. 

People’s training in Denmark and other 

societies 

The first questions concern the extent and context 

of training. How much of the adult population 

in Denmark is trained? Have adults in Denmark 

been trained during schooling or later in 

life? What was the organizational context of 

training of adults in Denmark, and has their 

training been voluntary or compulsory? 

Table 6.1 shows that only a small proportion of 

the adult population, 22%, has been trained. A 

minority of those trained received their training 

during schooling, and the training received during 

schooling was typically compulsory rather 

than voluntary. The majority of those trained 

received their training later in life, after their 

schooling. These people received their postschooling 

training in a variety of organizational 

contexts, their training was often informal, their 

training was also often received during further 

formal education, and then it was typically compulsory, 

rather than voluntary. Their training 

was less often training offered by colleges or 

universities, by business associations, by government 

agencies such as business advisory 

services or by employer, and in these organizational 

contexts their training was typically voluntary 

rather than compulsory. 

A person is thus considered trained if the person 

was trained during schooling or was trained 

later in life, as tapped by the initial questions in 

the above series in GEM. 

44

Table 6.1 

Training in starting a business 

Percentages of the adults surveyed in Denmark 2008 

Percent of Voluntary Compulsory 

adults training training 

Not trained 78 % 

Trained, any time in life 22 % 

Time of training; for those trained 

l Trained during schooling 

(up through gymnasium) 9 % 2 % 7 % 

l Trained later than schooling 14 % 

Organization of training; for those trained later 

l Formal education later than schooling 8 % 3 % 4 % 

l College or university 4 % 2 % 1 % 

l Association of businesses 3 % 3 % 1 % 

l Government agency 4 % 3 % 2 % 

l Employer 3 % 3 % 1 % 

l Informal training 8 % 

l Other source of training 4 % 3 % 1 % 

l Online training 2 % 

N = 2012 adult respondents 

Is training in Denmark more or less extensive 

than training in other societies? The extent of 

training in Denmark amounts to 22% of the 

adults, Table 6.1. The extent of training in other 

nations has also been measured, Table 6.2. The 

extent of training is far higher in several other 

developing countries surveyed, especially in 

Table 6.2 

Training of the adult population 

The three countries with highest percentage 

trained, the three countries lowest 

percentage trained and Denmark 2008 

Adults trained 

Finland 48 % 

Slovenia 36 % 

Belgium 33 % 

… 

Denmark 22 % 62 percentile 

… 

Israel 13 % 

Serbia 10 % 

Romania 8 % 

Finland, Slovenia and Belgium, and the extent 

is far less in several other countries, especially 

in Romania, Serbia and Israel. The extent of 

training in Denmark is a little above the middle 

or the typical, with 62% of the developed countries 

having lesser extent of training. 

In Denmark people have typically been trained 

later than their schooling, rather than during 

their schooling, Table 6.1. Indeed, 9% of the 

adults were trained during schooling whereas 

14% of the adults were trained later in life, a ratio 

of 1,5 as listed in Table 6.3. 

In other countries, has the training also been 

provided mainly after schooling, or has it been 

provided more frequently during schooling? 

The timing of training has also been measured 

in other countries, Table 6.3. In several other 

countries training has been provided upon 

schooling, far more than during schooling, especially 

in Japan, Finland and Serbia. In several 

other countries training has been provided 

more during schooling than afterwards in life, 

especially in Hungary, Belgium and Bosnia. In 

45

Table 6.3 

Training of people, during schooling and later 

The three countries with highest ratio of adults trained later to adults trained 

during schooling, the three countries with lowest ratio, and Denmark 2008 

Adults trained Adults Ratio 

during schooling trained later later/school 

Japan 4 % 16 % 3.5 

Finland 15 % 40 % 2.7 

Serbia 3 % 8 % 2.6 

… 

Denmark 9 % 14 % 1.5 62 percentile 

… 

Bosnia 13 % 11 % 0.8 

Belgium 23 % 18 % 0.8 

Hungary 17 % 10 % 0.6 

most countries the ratio of those trained later to 

those trained during schooling is lower than in 

Denmark. Among the developed countries, 62% 

are below Denmark in the ratio of those trained 

later to those trained during schooling. In simpler 

words, Denmark is atypical in its relatively 

high emphasis on training later, rather than during 

schooling. In yet other words, Denmark is 

atypical in its relatively low emphasis on training 

during schooling (this is consistent with the 

perceptions made by OECD, 2008). 

Gender and training 

Is training of people in Denmark dependent on 

their gender? Are males and females equally 

likely to receive training, or is one gender trained 

especially frequently? Table 6.4 shows that 

more of the men have been trained than of the 

women (the gender difference is statistically 

Table 6.4 

Training, by gender 

Adults in Denmark 2008 

highly significant with probability-value less 

than 0,0001 in a chi-square test). 

Is training dependent on gender also in other 

societies? Is the inequality between the genders 

in Denmark wider or narrower than in other 

societies? In Denmark, the ratio of male training 

to female training is 1,4 (namely 26% to 

19%), Table 6.5. In some societies, the inequality 

between the genders is wider, especially 

in France, Greece and Japan. In some countries, 

the ratio of males trained to females 

trained is lower, and in a few countries, the ratio 

is even in the opposite direction so that fewer of 

the men than of the women have been trained, 

notably in Latvia and Hungary. Denmark’s ratio 

of 1,4 is a little above the middle among the developed 

countries, several countries have wider 

inequality, but most of the other countries, 57%, 

have less inequality than Denmark. Thus, despite 

the egalitarian ideology in the Danish society, 

the inequality is actually comparatively 

wide. 

Men Women 

Trained 26 % 19 % 

Untrained 74 % 81 % 

Sum 100 % 100 % 

N adults 951 1.061 

46

Table 6.5 

Training of men and women 

The three countries with highest ratio of trained men to trained women, 

the three countries with lowest ratio, and Denmark 2008 

Men Women Ratio 

trained trained men/women 

France 24 % 12 % 2.0 

Greece 21 % 13 % 1.7 

Japan 21 % 14 % 1.5 

… 

Denmark 26 % 19 % 1.4 57 percentile 

… 

Finland 49 % 47 % 1.1 

Hungary 22 % 26 % 0.9 

Latvia 26 % 30 % 0.8 

Table 6.6 

Training by age 


18-24 years 25-34 years 35-44 years 45-54 years 55-64 years 

Trained 39 % 28 % 20 % 18 % 14 % 

Untrained 61 % 72 % 80 % 82 % 86 % 

Sum 100 % 100 % 100 % 100 % 100 % 

N adults 288 517 451 413 343 

Table 6.7 

Training of the people in each age-group 

The three countries with highest ratio of trained youngest to trained oldest, 

the three countries with lowest ratio, and Denmark 2008 

18-24 25-34 35-44 45-54 55-64 Ratio 

years years years years years youngtrained 

trained trained trained trained est/oldest 

Latvia 48 % 45 % 25 % 16 % 6 % 7.8 

Macedonia 4.5 

Italy 3.2 

… 

Denmark 39 % 28 % 20 % 18 % 14 % 2.8 81 percentile 

… 

Germany 1.1 

Greece 1.1 

Japan 14 % 12 % 18 % 19 % 23 % 0.6 

47

Age and training 

Does training differ among generations in 

Denmark and in other countries? Table 6.6 

shows that young people in Denmark are far 

more likely to have been trained than older 

people, thus age is negatively associated with 

the likelihood of having received training (the 

association is statistically highly significant). 

This is, of course, because training has become 

a component of formal education in recent 

decades, and also become offered in more 

other organizational contexts during recent 

decades in Denmark. The recent expansion of 

training thus manifests itself in how training 

depends on age in Denmark. The ratio of 

youngest trained to oldest trained is 2,8 (namely 

39% to 14%) and is indicative of the recent 

expansion of training among the young people 

in Denmark. 

Is training expanding likewise in other nations? 

In some nations, there is an even more penetrating 

expansion, notably in Latvia where many 

of the young people are trained but very few of 

the older people have been trained (as a legacy 

of the communist era, of course), Table 6.7. In 

most other countries there is less expansion of 

training, and, at the extreme, in Japan there is 

apparently no expansion of training among the 

young generations (consistent with Table 6.3). 

Denmark has a high ratio of trained youngest to 

trained oldest, 2,8, so only a few societies have 

greater expansion, whereas most societies 

(81% of the developed societies) have less expansion 

of training. 

This indicates that Denmark is among the societies 

with the fastest expansion of training. 

Education and training 

Is training dependent on education? In our survey 

of adults in Denmark, we ask people about 

their highest education, namely whether they 

have a vocational education, a short higher education 

(less than three years), a medium higher 

education (three to four years), a long higher 

education (more than four years), or neither vocational 

nor higher education (education will be 

examined in the next chapter). The frequency of 

training is shown in Table 6.8, for each educational 

level. 

Training is most frequent among those with vocational 

education or short further education, 

and training is least frequent among those with 

long higher education (differences among educational 

levels are statistically highly significant, 

with probability value 0,003 in a chi-square 

test). Perhaps surprisingly, the people with the 

longest education are the ones least frequently 

trained. 

Table 6.8 

Training in entrepreneurship, by education 


Vocational Short higher Medium higher Long higher Neither 

education education education education vocational 

nor higher 

education 



Sum 100 % 100 % 100 % 100 % 100 % 

N adults 481 194 505 332 474 

48

Figure 6.2 

Estimated effects upon people’s training from their gender, age and education 


Gender (male) 

Age 


Medium higher 

education 

Short higher 

education 

Vocational 

education 

Neither vocational 

nor higher education 

Effects upon training from gender, age and 

education 

In the above, we examined how gender is related 

to training, how age is related to training, 

and how education is related to training. Now 

we ask the further question; how is training affected 

by each of these attributes, when holding 

the other attributes constant? These separate 

effects are estimated by a regression (Table 

6.9 in the Appendix). 

Age has a strong effect that is negative; older 

people are less likely to have been trained than 

younger people, also when controlling for gender 

and education. Gender has a notable effect, 

being male increases odds of being trained. The 

effects of education are relative to long higher 

education. Having a medium higher education 

enhances the odds of being trained (relative to 

having a long higher education), having a short 

education enhances odds even more, having a 

vocational training also enhances the odds considerably, 

and having neither vocational nor 

higher education may reduce the odds (relative 

to having a long higher education), when controlling 

for gender and age. 

These effects are shown in Figure 6.2, where a 

positive effect is shown as a solid arrow, a negative 

effect is shown as a dashed arrow, and the 

magnitude of an effect is indicated by the thickness 

of the arrow. 

This chapter has examined conditions of training. 

In turn, training has consequences for entrepreneurship. 

Effects of training upon entrepreneurship 

will be examined in the next chapters. 

49

Chapter 7 

People’s education, training, competency and networking in Denmark 


only through training, but also through their 

education, competency and networking? More 

specifically, this chapter deals with the issues, 

Are people in Denmark educated at high and diverse 

levels in the educational system? Are people 

typicially competent to start a business? Is 

people’s competency enhanced by their education 

and training? Are people typically networking 

with starters? Is people’s networking promoted 

by their education, training and competency? 

We shall thus examine the effects among people’s 

education, training, competency and networking. 

The effect of education upon training 

was examined in the preceding chapter. How all 

this affects vocation and entrepreneurship will 

be examined in subsequent chapters. 

The idea of effects among people’s education, 

training, competency and networking can be 

symbolized as a path-diagram, in which a hypothesized 

effect is drawn as an arrow, Figure 

7.1. The task then is to estimate each distinct 

effect, preferably while other things are equal, 

and specifically ascertain whether the effect is 

different from zero, whether positive or negative, 

and to estimate the magnitude of the effect. 

Education is measured, in our annual survey in 

Denmark, by asking the adults, 

What is your completed vocational education 

or further education?Is it … 

vocational education [erhvervsuddannelse], 

further education for less than three years 

[videregående uddannelse på under tre år], 

further education for three to four years [videregående 

uddannelse på tre til fire år], 

further education for more than four years 

[videregående uddannelse på over fire år], or 

neither vocational education nor higher education? 

Training is measured, in our survey in 2008, by 

asking the adults, 

Have you ever taken part in training in starting 

a business at primary or secondary school? 

Figure 7.1 

Hypothesized effects among people’s education, training, competency and networking 



Competency 

Networking 

50


a business after you completed your education 

in school? 

Training was analyzed in the preceding chapter. 

We consider a person trained if the response is 

yes to either of the two above questions. 

Competency is measured by asking the adults, 

Do you have the knowledge, skills and experience 

required to start a new business? 

Networking with entrepreneurs is measured by 

asking the adults, 

Do you know someone personally who has 

started a business in the past two years? 

People in Denmark have typically pursued further 

education beyond their schooling, either 

vocational education or higher education of 

short, medium or long duration, Table 7.1. 

People’s education affects their likelihood of 

training in entrepreneurship, in Denmark, as examined 

in the previous chapter. People with vocational 

training are especially likely to be 

trained, whereas people with long higher education 

are least likely to be trained. 

People’s competency related to their 

education, and related to their training 

In Denmark, people’s education is also related 

to their entrepreneurial competency to start a 

business, Table 7.2. People with long higher 

education are far more likely to have this competency 

than people who have neither vocational 

nor higher education (differences among 

educational groups are statisticially highly significant, 

with probability-value less than 0.0001 

in a chi-square test). 

Table 7.1 

Highest level of education 

Adults in Denmark 2003-08 

Percent 

Vocational education 29 % 

Short higher education 9 % 

Medium higher education 24 % 

Long higher education 18 % 

Neither vocational nor higher 

education 21 % 

Sum 100 % 

N adults 19.934 

People’s competency not only depends on their 

educational level, but also on their training, Table 

7.3. Trained people are of course far more 

likely to be competent than people who have 

Table 7.3 

Competency, by training 


Trained Untrained 

Competent 50 % 31 % 

Less competent 50 % 69 % 

Sum 100 % 100 % 

N adults 446 1.499 

Table 7.2 

Competency, by education 




nor higher 

education 

Competent 44 % 35 % 40 % 50 % 28 % 

Less competent 56 % 65 % 60 % 50 % 72 % 

Sum 100 % 100 % 100 % 100 % 100 % 

N adults 5.091 1.597 4.281 3.207 3.544 

51

Table 7.4 

Networking, by education 




nor higher 

education 

Networking with 

starter 45 % 44 % 47 % 55 % 41 % 

Not networking 55 % 56 % 53 % 45 % 59 % 

Sum 100 % 100 % 100 % 100 % 100 % 

N adults 5.231 1.645 4.412 3.282 3.649 

Table 7.5 

Networking, by training 




starter 58 % 46 % 

Not networking 42 % 54 % 

Sum 100 % 100 % 

N adults 467 1.528 

Table 7.6 

Networking, by competency 


Competent Less 

competent 


starter 58 % 39 % 

Not networking 42 % 61 % 

Sum 100 % 100 % 

N adults 7.056 10.626 

not been trained (the difference is statistically 

highly significant, with probability-value less 

than 0.0001 in a chi-square test). 

People’s networking related to their 

education, to their training, and to their 

competency 

People’s education also affects their networking, 

whether or not they know a starter, Table 

7.4. People with long higher education are most 

likely to know a starter (differences among educational 

groups are statistically highly significant, 

with probability-value less than 0.0001 in 

a chi-square test). 

Networking depends not only on education, but 

also on training, Table 7.5. Trained people are 

much more likely than untrained people to know 

a starter (the difference is statistically highly 

significant, with probability-value less than 

0.0001 in a chi-square test). 

Networking depends not only on education and 

training, but also on competency, Table 7.6. 

Competent people are much more likely to 

know a starter than less competent people (the 

difference is statistically highly significant, with 

probability-values less than 0.0001 in a chisquare 

test). 

Each of the above cross-tabulations shows the 

relationship between two phenomena, while 

ignoring everything else that might also cause 

the relationship. This limitation of the above 

analyses will now be remedied by a more elaborate 

analysis. 

Effects among people’s education, training, 

competency and networking 

The question now is, What is each distinct effect 

upon training, upon competency and upon 

networking, while holding other things constant? 

The effects upon training from educa- 

52

tion, while holding gender and age constant, 

were estimated in the preceding chapter (Figure 

6.2). 

The effects upon competency from training and 

education while controling for gender and age, 

are estimated by regression (Table 7.8 in the Appendix). 

Competency is promoted by training 

(when education, gender and age are held constant). 

Competency is also affected by education 

in the manner that medium higher education 

makes for less competency than long higher 

education, short higher education makes for less 

competency than long higher education, vocational 

education entails less competency than 

long higher education, and having neither vocational 

education nor higher education makes for 

much less competency than long higher education 

(controlling for gender, age and training). 

The effects upon networking are also estimated 

by a regression (Table 7.9 in the appendix). Networking 

is promoted by competency and by 

training (while holding other conditions constant). 

Networking is affected by education in 

the following ways. Medium and short higher 

education and also vocational education entail 

less likelihood of networking than long higher 

education, and having neither vocational nor 

higher education entails much less chance of 

networking than long higher education (holding 

other conditions equal). 

These effects are all shown in Figure 7.2, for 

people in Denmark, where solid arrows show 

positive effects, dashed arrows show negative 

effects, and the magnitude of an effect is indicated 

by the thickness of the arrow. 

People’s education, training, competency and 

networking have consequences for entrepreneurship. 

How people’s education, training, 

competency and networking affect their vocation 

– to be an entrepreneur or not – shall be 

examined in the next chapter. 

Figure 7.2 

Estimated effects among people’s education, training, competency and networking 

Adults in Denmark 


Medium higher 

education 

Competency 

Short 

higher education 

Vocational 

education 


Neither vocational nor 

higher education 

53

Chapter 8 

People’s vocation shaped by education, training, competency and 

networking 


shaped by their learning? More specifically, 

this chapter addresses the questions, How is 

people’s vocation – entrepreneur or not – affected 

by their education, by their training, by their 

competency and by their networking? 

The idea of effects upon people’s vocation from 

their education, training, competency and networking 

can be symbolized as a path-diagram, 

in which a hypothesized effect is drawn as an 

arrow, Figure 8.1. The task then is to estimate 

each distinct effect, preferably while other 

things are equal, and specifically ascertain 

whether the effect is different from zero, whether 

positive or negative, and to estimate the 

magnitude of the effect. 

The effects among people’s education, training, 

competency and networking were estimated in 

the preceding chapter, so here we focus on vocation. 

Entrepreneur denotes the vocation of preparing 

or running a business. More specifically, entrepreneur 

encompasses several specialized types 

of entrepreneur, as considered in Chapter 1 and 

Chapter 3, and here shown in Table 8.1 (here 

’entrepreneur’ does not include ex-owners and 

investors). The entrepreneurs make up about 

18% of the adult population in Denmark. The 

non-entrepreneurs are the vast majority of the 

adult population. Here we just consider two vocations, 

entrepreneur versus non-entrepreneur. 

Figure 8.1 

Hypothesized effects upon people’s vocation from their education, training, competency and 




Competency 


Vocation 

54

Table 8.1 

Vocation of adults 

Denmark 2003-08 

Percent 

Non-entrepreneurs 82 % 

Entrepreneurs 18 % 

Breakdown by type of entrepreneur 

prospective entrepreneurs 8 % 

autonomous starters 3 % 

jobrelated starters 3 % 

new-business owner-managers 4 % 

established-business owner-managers 6 % 

Sum 100 % 

N adults 20.087 

We now examine how vocation is related to 

each condition, that is, how vocation relates to 

education, how vocation relates to training, 

how vocation relates to competency, and how 

vocation relates to networking. 

Vocation related to education, related to 

training, related to competency, and related 

to networking 

People’s vocation – being an entrepreneur or 

not – is related to their education, Table 8.2. 

People with long higher education have highest 

probability of being entrepreneurs; whereas 

people with neither vocational nor higher education 

have the lowest probability of being entrepreneurs 

(differences among educational 

groups are statistically highly significant, with 

Table 8.3 

Vocation of adults, by their training 

Denmark 2008 

Trained Not trained 

Non-entrepreneurs 79 % 86 % 

Entrepreneurs 21 % 14 % 

Sum 100 % 100 % 

N adults 471 1.541 

probability-value less than 0.0001 in a chisquare 

test). 

People’s vocation is also related to their training, 

Table 8.3. Trained people are of course 

much more likely than untrained people to be 

entrepreneurs (the difference is statistically 

highly significant, with probability-value 0.0001 

in a one-sided chi-square test). 

People’s vocation is also related to their competency 

to start a business, Table 8.4. Competent 

people are of course much more likely than less 

competent people to be entrepreneurs (the difference 

is statistically highly significant, with 

probability-value less than 0.0001 in a one-sided 

chi-square test). 

People’s vocation is also related to their networking, 

knowing a starter, Table 8.5. Networking 

people are of course much more likely than 

not networking people to be entrepreneurs (the 

difference is statistically highly significant, with 

Table 8.2 

Vocation of adults, by their level of education 




nor higher 

education 

Non-entrepreneurs 82 % 82 % 84 % 75 % 87 % 

Entrepreneurs 18 % 18 % 16 % 25 % 13 % 

Sum 100 % 100 % 100 % 100 % 100 % 

N adults 5.726 1.794 4.730 3.527 4.157 

55

Table 8.4 

Vocation of adults, by their competency 



competent 


Entrepreneurs 36 % 8 % 

Sum 100 % 100 % 

N adults 7.122 10.715 

Table 8.5 

Vocation of adults, by their networking 



Not 



Entrepreneur 29 % 10 % 

Sum 100 % 100 % 

N adults 8.502 9.842 

Vocation is shaped by competency, in the manner 

that competent people are far more likely to 

be entrepreneurs than less competent people 

(holding other conditions constant). Vocation is 

also shaped by networking, in the manner that 

networking people are more likely to be entrepreneurs 

than non networking people. Training 

seems to have a tiny effect on vocation. Education 

affects vocation in the following ways: 

people with medium or short higher education 

or with vocational education have less likelihood 

of becoming entrepreneurs than people 

with long higher education. Also people with 

neither vocational nor higher education have 

less likelihood of becoming entrepreneurs than 

people with higher education (controling for 

the other conditions). 

These effects are shown in Figure 8.2, which 

also shows the effects found in the preceding 

chapter (Figure 7.2). A solid arrow denotes a 

positive effect, a dashed arrow shows a negative 

effect, and the thickness of an arrow symbolizes 

the magnitude of the effect. 

Vocation can thus be partly understood and explained 

in terms of four factors: education, 

training, competency and networking. People 

with long higher education, with training, with 

competency, and with a network are more likely 

than other people to be entrepreneurs. 

probability-value less than 0.0001 in a one-sided 


The above cross-tabulations show how vocation 

relates to each condition, while ignoring 

the other conditions. This limitation will now be 

overcome by a more elaborate analysis. 

Having studied people, as they may or may not 

be involved in entrepreneurship, we now zoom 

in to focus on the entrepreneurs. 

Effects upon people’s vocation from their 

education, training, competency and 


Now we address the further question, how is 

vocation affected by each condition, while holding 

the other conditions constant? The distinct 

effects are estimated by regression (Table 8.6 in 

the appendix). 

56

Figure 8.2 

Estimated effects upon people’s vocation from their education, training, competency and 




Medium higher 

education 

Competency 

Short higher 

education 

Vocational 

education 




Vocation 

57

Part 4 

entrepreneurs 

Chapter 9 

Entrepreneurs’ education, training, competency and networking in 

Denmark 

How are Danish entrepreneurs learning? More 

specifically, this chapter addresses the questions, 

Are entrepreneurs educated at different 

levels than other people? Are entrepreneurs 

typically train ed to start a business, whereas 

other people are typically untrained? Are entrepreneurs 

typically competent to start a business, 

whereas other people are typically not 

competent? Are entrepreneurs networking with 

a wide spectrum of advisors? What are the effects 

among entrepreneurs’ education, training, 

competency and networking? 

People and their learning were examined in 

the previous chapters. This chapter focuses on 

Danish entrepreneurs and their learning. The 

impact of the entrepreneurs’ learning on their 

performance will be examined in the subsequent 

chapters. 

The idea of effects among entrepreneurs’ education, 

training, competency and networking 

can be symbolized as a path-diagram, in which 

a hypothesized effect is drawn as an arrow, Figure 

9.1. The task then is to estimate each distinct 

effect, preferably while other things are 

equal, and specifically to ascertain whether the 

effect is different from zero, positively or negatively, 


Figure 9.1 

Hypothesized effects among entrepreneurs’ education, training, competency and networking 



Competency 


59

An entrepreneur here denotes a person who intends 

to start a new business, who is starting a 

venture, or who is owning and managing an existing 

firm, those called prospective entrepreneurs, 

starters and owner-managers (Chapters 

1 and 3). However, the prospective entrepreneurs 

will be ignored when focusing on networks 

of advice concerning the business. 

Education is measured, in our annual survey in 

Denmark, by asking, 

What is your completed vocational education 

or further education?Is it … 

vocational education [erhvervsuddannelse], 

further education for less than three years 

[videregående uddannelse på under tre år], 

further education for three to four years [videregående 

uddannelse på tre til fire år], 

further education for more than four years 

[videregående uddannelse på over fire år], 

or 

neither vocational education nor higher education? 

Training is measured by asking, 


a business at primary or secondary school? 


a business after you completed your education 

in school? 

An entrepreneur is considered trained if the response 

is yes to either of the two questions. 

Competency is measured by asking, 

Do you have the knowledge, skill and experience 

required to start a new business? 

The above questions were posed not only to entrepreneurs, 

but to all surveyed adults, as was 

examined in Chapter 7. 

Entrepreneurs’ networking is measured in some 

detail. I asked starters: Various people may give 

advice on your new business. Have you received 

advice from … 

spouse or partner 

parents 

other family 

friends 

current work-colleagues 

earlier work-colleagues 

current boss 

a former boss 

someone in another country 

someone starting a business 

someone with much business experience 

someone with expertise in what you do 

a researcher or inventor 

a possible investor 

a bank 

a lawyer 

an accountant 

a public advising service for business 

a firm you collaborate with 

a firm you compete with 

a supplier 

a customer 

Owner-managers are asked in the same manner: 

Various people may give advice on your 

business. During the last year, have you received 

advice from … answering for the same list of 22 

possible advisors. 

Networking will here, be measured as the size of 

the entrepreneur’s network, the number of advisors 

used by the entrepreneur, ranging from 0 to 

22. This measure of an entrepreneur’s networking 

is better than the measure of a person’s networking 

used in previous chapters, where a person’s 

network was measured by asking about 

knowing or not knowing someone who is starting, 

a measurement that is informative for analyzing 

people’s vocational choice, but insufficient 

when focusing on entrepreneurs. 

Entrepreneurs’ education 

Entrepreneurs’ educational background can be 

compared to the educational background of 

non-entrepreneurs, Table 9.1 (the difference between 

entrepreneurs and non-entrepreneurs is 

statistically highly significant, with probabilityvalue 

less than 0.0001 in a chi-square test). Entrepreneurs 

have long higher education, more 

frequently than non-entrepreneurs. Conversely, 

entrepreneurs infrequently have neither higher 

nor vocational education, compared to non-entrepreneurs. 

The typical entrepreneur has either 

vocational education or long higher education. 

In other words, educationally, there are 

two major groups of entrepreneurs, those with 

60

Table 9.1 

Education, by vocation 


Entre- Non-entrepreneurs 

preneurs 

Vocational education 29 % 29 % 

Short higher education 9 % 9 % 

Medium higher educ. 22 % 24 % 

Long higher education 25 % 16 % 

Neither higher nor 

vocational education 15 % 22 % 

Sum 100 % 100 % 

N adults 3.537 16.397 

competent to start a business than non-entrepreneurs, 

Table 9.3. (the difference is statistically 

highly significant, with probability-value 

Table 9.3 

Competency, by vocation 



preneurs 



Sum 100 % 100 % 

N adults 3.414 14.423 

vocational education and those with long higher 

education. 

Entrepreneurs’ training 

Entrepreneurs are of course more frequently 

trained than non-entrepreneurs, Table 9.2 (the 

difference between entrepreneurs and non-entrepreneurs 

is statisticially highly significant, 

with probability-value 0.0001 in a one-sided chisquare 

test). Training, for both entrepreneurs and 

non-entrepreneurs took place mainly later than 

schooling than during schooling. Indeed, few entrepreneurs 

were trained during their schooling. 

Almost as many of the non-entrepreneurs were 

trained during their schooling, so peoples’ vocational 

choice – to become an entrepreneur or not 

– has hardly been affected by whether or not they 

got training during their schooling. 

Entrepreneurs’ competency 

Entrepreneurs are of course more frequently 

Table 9.2 

Training, by vocation 



preneurs 

Not trained 68 % 78 % 

Trained 32 % 22 % 

Sum 100 % 100 % 

N adults 310 1.702 

Table 9.4 

Entrepreneurs’ networking with advisors 

Starters and owner-managers 

in Denmark 2008 

Entrepreneurs 

advised 

Spouse or life-partner 45 % 

Parents 19 % 

Other family 21 % 

Friends 50 % 

Current work-colleagues 49 % 

Earlier work-colleagues 32 % 

Current boss 27 % 

An earlier boss 18 % 

Someone in another country 23 % 

Someone starting a business 27 % 

Someone with much business 

experience 53 % 

Someone with expertise in what 

you do 61 % 

A researcher or inventor 13 % 

A possible investor 16 % 

A bank 29 % 

A lawyer 23 % 

An accountant 50 % 

A public advising service for business 18 % 

A firm you collaborate with 36 % 

A firm you compete with 17 % 

A supplier 34 % 

A customer 41 % 

Mean size of the network 7.0 

N starters and owner-managers 234 

61

less than 0.0001 in a one-sided chi-square test). 

By far most entrepreneurs are competent, and 

by far most non-entrepreneurs do not have this 

competency. 

Entrepreneurs’ networking with advisors 

Entrepreneurs network with a variety of advisors, 

Table 9.4. The entrepreneurs most frequently 

receive advice from someone with expertise 

in what the entrepreneur does, from 

someone with much business experience, from 

an accountant, and from friends. On average, 

the entrepreneurs receive advice from 7 kinds 

of advisors, but some entrepreneurs have much 

larger networks whereas other entrepreneurs 

have much smaller networks. 

higher education have less frequently than others 

been trained. This appears to be the one notable 

difference, but our sample is so small that 

the rather small differences are not statistically 

significant (the probability-value is 0.37 in the 


Entrepreneurs’ competency is related to their 

education, Table 9.6. Entrepreneurs with vocational 

or long higher education consider themselves 

competent more frequently than entrepreneurs 

who have neither vocational nor higher 

education, which is not surprising (educational 

groups differ we conclude with considerable 

statistical confidence, the probability-value 

is 0.0002 in a chi-square test). 

Relations among entrepreneurs’ education, 


Entrepreneurs’ training appears related to their 

education, Table 9.5. Entrepreneurs with long 

Entrepreneurs’ competency also appears related 

to their training, Table 9.7. Trained entrepreneurs 

appear to be competent more often than 

untrained entrepreneurs, which is not surpris- 

Table 9.5 

Training of entrepreneurs, by their education 

Entrepreneurs in Denmark 2008 

Vocational Short further Medium further Long higher Neither 


nor higher 

education 



Sum 100 % 100 % 100 % 100 % 100 % 

N entrepreneurs 69 39 76 70 51 

Table 9.6 

Competency of entrepreneurs, by their education 

Entrepreneurs in Denmark 2003-08 



nor higher 

education 

Competent 78 % 71 % 75 % 78 % 69 % 

Less competent 22 % 29 % 25 % 22 % 31 % 

Sum 100 % 100 % 100 % 100 % 100 % 


62

Table 9.7 

Competency of entrepreneurs, by their 

training 


Trained Not trained 



Sum 100 % 100 % 

N entrepreneurs 94 206 

ing. However, the difference in the small sample 

is not statistically significant (the probability 

value is 0.18 in a one-sided chi-square test). 

Entrepreneurs have a network of advisors with 

a size that may be small or large. Is entrepreneurs’ 

network size related to their education? 

Table 9.8 distinguishes between large networks 

and small networks and also calculates the average 

network size within each educational 

group. Our sample of entrepreneurs within each 

educational group is small, so small as to render 

inferences hard. The size of the entrepreneurs’ 

network appears not to be systematically related 

to their education in a statistically significant 

manner (the probability-value is 0.24 for the 

chi-square test of the cross-tabulation, and the 

p-value is 0.19 in the anova F-test of differences 

among the five group-means). In further research 

during 2009, I will examine the relationship 

of entrepreneurs’ education to their networking. 

Table 9.9 

Size of entrepreneurs’ advisory network, 

by their training 

Starters and owner-managers in 

Denmark 2008 


Large network 64 % 38 % 

Small network 36 % 62 % 

Sum 100 % 100 % 

Mean size 8,5 6,3 


Entrepreneurs’ advisory network is related to 

their training, Table 9.9. Trained entrepreneurs 

network more extensively than untrained entrepreneurs 

(the difference is statistically highly 

significant, with p-value 0.0002 in the one-sided 

chi square test of the cross-tabulation, and p- 

value 0.0002 in a one-sided t-test of the difference 

between the two means). 

Entrepreneurs’ network is also related to their 

competency to start a business, Table 9.10. 

Competent entrepreneurs network more extensively 

than entrepreneurs who do not have this 

competency (the difference is statistically significant, 

with p-value 0.02 in the one-sided chi 

square test of the cross-tabulation, and p-value 

0.01 in a one-sided t-test of the difference between 

the two means). 

Table 9.8 

Size of entrepreneurs’ advisory network, by their education 

Starters and owner-managers in Denmark 2008 



nor higher 

education 

Large network 52 % 27 % 50 % 42 % 50 % 

Small network 48 % 73 % 50 % 58 % 50 % 

Sum 100 % 100 % 100 % 100 % 100 % 

Mean size 7,4 5,9 7,5 6,1 7,8 


63

Table 9.10 

Size of entrepreneurs’ advisory network, 

by their competency 

Starters and owner-managers in 

Denmark 2008 


competent 

Large network 50 % 33 % 

Small network 50 % 67 % 

Sum 100 % 100 % 

Mean size 7,4 5,7 


The above cross-tabulations have examined the 

relationship between two phenomena at a time, 

ignoring that the relationship may be affect by 

other conditions. The above analyses are thus 

preliminaries to an analysis that considers each 

phenomenon to have several causes. This more 

elaborate analysis comes next. 

Effects among entrepreneurs’ education, 


The question now is, what are the effects among 

entrepreneurs’ education, training, competency 


More precisely, how is network size affected 

jointly by competency, training and education? 

How is competency affected jointly by training 

and education? How is training affected by education, 

while also taking other conditions into 

consideration, such as gender and age of the 

entrepreneurs? 

what the effects of education are upon networking. 

The effects upon entrepreneurs’ competency 

from training and education are estimated by 

regression (Table 9.12 in the Appendix). Entrepreneurs’ 

training promotes their competency, 

as we would expect. The frame of reference for 

ascertaining the effects of education is long 

higher education. Entrepreneurs with medium 

or short higher education tend to be less competent 

than entrepreneurs with long higher education. 

Entrepreneurs who have neither vocational 

nor higher education also tend to be less 

competent than entrepreneurs with long higher 

education. Vocationally educated entrepreneurs 

tend to be as competent as entrepreneurs 

with long higher education. 

The effects upon entrepreneurs’ training from 

education are estimated by regression (Table 

9.11 in the Appendix). Entrepreneurs with vocational 

education, with short or medium higher 

education, or without vocational or higher education 

have received training more frequently 

than entrepreneurs with long higher education. 

In other words, entrepreneurs with long higher 

education have especially rarely been trained in 

starting a business. 

These estimated effects are shown in Figure 

9.2, where a positive effect is shown as a solid 

arrow and a negative effect as a dashed arrow, 

and the magnitude of an effect is shown by the 

thickness of the arrow. 

The effects upon entrepreneurs’ network size 

from competency, training and education are 

estimated by regresssion (Table 9.13 in the 

Appendix). Training has a very large and positive 

effect upon networking. Competency also 

has a large and positive effect upon networking. 

The effects of the various levels of education 

are hard to estimate because the size of 

the sample in each educational group is small, 

so small that we do not obtain statistical significance 

when testing the effects. So we cannot 

conclude, with any reasonable confidence, 

64

Figure 9.2 

Estimated effects among entrepreneurs’ education, training, competency and networking 

Entrepreneurs in Denmark (effects upon networking from education are omitted) 


Medium higher 

education 

Competency 

Short higher 

education 

Vocational 

education 




65

Chapter 10 

Entrepreneurs’ innovativeness expanded by education, training, 


How is entrepreneurs’ innovativeness shaped 

by their learning? More specifically, this chapter 

addresses the questions, What are the distinct 

effects upon innovativeness from education, 

from training, from competency and from networking? 

The idea of effects upon entrepreneurs’ innovativeness 

from their education, training, competency 

and networking can be symbolized as a 

diagram with sequences of arrows, Figure 10.1. 

Each arrow here symbolizes a hypothesized effect. 

The effect may be positive or negative, and 

of large or small magnitude, or it might actually 

be nill. The task then is to estimate each distinct 



different from zero, positively or negatively, and 

to estimate the magnitude of the effect. These 

effects shall be estimated in this chapter. 

Innovativeness is measured by asking starters 

and owner-managers three questions, 

Will all, some, or none of your potential customers 

consider this product or service new 

and unfamiliar? 

Right now, are there many, few, or no other 

businesses offering the same products or 

services to your potential customers? 

Have the technologies or procedures required 

for this product or service been available for 

less than a year, or between one to five years, 

or longer than five years? 

Figure 10.1 

Hypothesized effects upon innovativeness from education, training, competency and 




Competency 


Innovativeness 

66

The response to each question can be coded on 

a scale from 1 to 3 for increasing innovativeness 

(the first question is coded 1 for ”none”, 2 

for ”some” and 3 for ”all”; the second question 

is coded 1 for ”many”, 2 for ”few” and 3 for ”no 

others”; and the third question is coded 1 for 

”longer than five years”, 2 for ”between one to 

five years” and 3 for ”less than a year”). The 

responses to the three questions are correlated 

positively, so innovativeness on one question 

tends to go hand in hand with innovativeness 

on another question. Therefore a reasonably 

good overall measure of an entrepreneur’s innovativeness 

is the average of the three responses 

given by the entrepreneur. This measure 

of innovativeness ranges from 1 to 3. Entrepreneurs 

with an average of at least 2 shall be 

called innovative, and entrepreneurs with an 

average below 2 are called less innovative. 

Here we first examine the relationship between 

innovativeness and education, then examine 

the relationship between innovativeness and 

training, then the relationship between innovativeness 

and competency, and then the relationship 

between innovativeness and networking. 

Such a relationship is first examined while ignoring 

other conditions that may impact the relationship. 

Going beyond this limitation in a 

more elaborate analysis, we then examine how 

innovativeness is related to all four conditions 

simultaneously, and estimate the effect of each 

condition, while holding the other conditions 

constant. 

Innovativeness and education 

Innovativeness is related to education, Table 

10.1. Entrepreneurs with long education are 

most innovative, on average, while entrepreneurs 

with vocational training are least innovative, 

which is not surprising (differences among 

educational groups are statistically significant, 

the probability-value is 0.001 in the chi-square 

test for the cross-tabulation, and the p-value is 

0.0001 in the anova F-test comparing the mean 

values of innovativeness, listed at the bottom of 

the table). 

Innovativeness and training 

Is innovativeness related to training? We would 

expect that training enhances innovativeness. 

The evidence, however, does not demonstrate 

this, Table 10.2. Our sample is small (because 

training was measured only in 2008), so small 

that it can hardly reveal any difference between 

the trained and the untrained entrepreneurs. 

Table 10.2 

Innovativeness of entrepreneurs, by their 


Denmark 2008 


Innovative 36 % 37 % 

Less innovative 64 % 63 % 

Sum 100 % 100 % 

Mean innovativeness 1,6 1,5 


Table 10.1 

Innovativeness of entrepreneurs, by their education 




nor higher 

education 

Innovative 32 % 37 % 41 % 43 % 39 % 

Less innovative 68 % 63 % 59 % 57 % 61 % 

Sum 100 % 100 % 100 % 100 % 100% 

Mean innovativeness 1.5 1.6 1.7 1.7 1.6 


67

Innovativeness and competency 

Innovativeness is related to competency to start 

a business, Table 10.3. Competent entrepreneurs 

tend to be more innovative than entrepreneurs 

without this competency, as we should 

expect (the difference is statistically significant; 

the probability-value is 0.04 for the one-sided 

chi-square test of the cross-tabulation, and the 

p-value is 0.01 for comparing the two means in 

a one-sided t-test). 

Table 10.3 


competency 



competent 



Sum 100 % 100 % 

Mean innovativeness 1.62 1.55 

N respondents 1.165 368 

Innovativeness and networking 

Innovativeness is related to networking with 

advisors, Table 10.4. Entrepreneurs with many 

advisors tend to be more innovative than entrepreneurs 

with few advisors, as we would expect. 

The correlation between innovativeness 

and size of the network is positive it is 0.2 (and 

is statistically significant with probability-value 

0.02 in a one-sided test). 

Table 10.4 


networking with advisors 

Denmark 2008 

The above cross-tabulations are limited, in that 

they only examine association between two 

phenomena at a time, and ignore other tings 

that may impact on the association. We shall 

therefore extend the analysis in the following 

section. 

Effects upon innovativeness from education, 


The question now is, how is innovativeness 

shaped by distinct effects from education, training, 

competency and networking? 

The effects upon innovativeness from education, 

training, competency and networking are 

estimated by regression (Table 10.5 in the Appendix). 

Innovativeness is greatly promoted by 

networking, and also promoted by competency 

and training. Entrepreneurs with vocational education, 

or short or medium higher education, 

or without vocational or higher education are 

less innovative than entrepreneurs with long 

higher education. In other words, entrepreneurs 

with long higher education are especially innovative. 

These effects are to be expected, but it is 

important to note that they are so strong as to 

be discerned and be distinct from one another 

and from other conditions. 

The effects among education, training, competency 

and networking were estimated in the 

preceding chapter (Figure 9.2). The effects of 

education upon networking, though, are omitted. 

The estimated effects are shown in Figure 10.2, 

where a solid arrow denotes a positive effect, a 

dashed arrow symbolizes a negative effect, and 

the thickness of an arrow represents the magnitude 

of the effect. 

Small Large 

network network 



Sum 100 % 100 % 

Mean innovativeness 1,4 1,6 


68

Figure 10.2 

Estimated effects upon innovativeness from education, training, competency and networking 

Starters and owner-managers in Denmark 


Medium higher 

education 

Competency 

Short higher 

education 

Vocational 

education 




Innovativeness 

69

Chapter 11 

Entrepreneurs’ exports expanded by education, training, competency and 


How is entrepreneurs’ exporting formed by their 

learning? More specifically, this chapter addresses 

the questions, What are the separate effects 

upon export-orientation from education, from 

training, from competency and from networking? 

The idea of effects upon entrepreneurs’ exports 

from their education, training, competency and 

networking can be symbolized as a diagram 

with sequences of arrows, Figure 11.1. Each arrow 

here symbolizes a hypothesized effect. The 

effect may be positive or negative, and of large 

or small magnitude, or it might actually be nill. 

The task then is to estimate each distinct effect, 

preferably while other things are equal, and 

specifically ascertain whether the effect is dif- 

ferent from zero, positively or negatively, and to 

estimate the magnitude of the effect. These effects 

shall be estimated in this chapter. 

Entrepreneurs’ education, training, competency 

and advisory network were examined in Chapter 

9, so here we focus on exports. Export-orientation 

is measured by asking the starters and 

owner-managers, 

What proportion of your customers will normally 

live outside your country? Is it more than 

90%, more than 75%, more than 50%, more 

than 25%, more than 10%, or 10% or less [or 

none]? 

Entrepreneurs differ greatly in their export-ori- 

Figure 11.1 

Hypothesized effects upon exporting from education, training, competency and networking 



Competency 


Exporting 

70

entation, Table 11.1. Entrepreneurs are typically 

more oriented toward the home-market than toward 

exports. Indeed, more than half of the entrepreneurs 

have no customers abroad. The 

export-orientation is a little stronger among 

starters than among owner-managers, so businesses 

are not merely born globals, but they 

are more often conceived globals! The entrepreneurs 

reported their export-orientation in 

terms of percentage-groups. By using the midpoint 

of each percentage-group, each entrepreneur’s 

export-orientation is estimated as a percentage, 

so we can calculate the average of 

their percentages, and also take the logarithm 

of the percentages, and then average (Table 

11.1). The logarithmic measurement of exportorientation 

reduces the skewness and avoids 

outliers, so this measure will be used extensively 

for this investigation. 

The starters and owner-managers are rather 

similar in their export-orientation, so in the following 

they shall be analyzed jointly. First, we 

shall examine how export-orientation is related 

to each condition while ignoring all other conditions. 

Then, finally and more elaborately, we 

shall examine all effects, while also considering 

other conditions. 

Export-orientation and education 

Entrepreneurs’ export-orientation is related to 

their education, Table 11.2. Entrepreneurs with 

Table 11.1 

Entrepreneurs’ exports to customers abroad 

Starters and owner-managers in Denmark 2003-08 

Starters 

Owner-managers 

91-100 % of customers abroad 7 % 4 % 






No customers abroad 53 % 54 % 

Sum 100 % 100 % 

Mean percent customers abroad 16 % 14 % 

Mean of logarithm of export 1.4 1.3 

N entrepreneurs 497 1.677 

Table 11.2 

Exporting by entrepreneurs, by their education 




nor higher 

education 

Exporting 42 % 38 % 45 % 53 % 45 % 

Not exporting 58 % 62 % 55 % 47 % 55 % 

Sum 100 % 100 % 100 % 100 % 100% 

Mean log export 1.2 1.1 1.3 1.5 1.3 


71

The distinct effects upon exporting are estimatlong 

higher education are more export-oriented 

than entrepreneurs with vocational education 

and short further education (the difference between 

the educational groups is statistically 

significant; with probability-value 0.002 in the 

chi-square test of the cross-tabulation, and p- 

value 0.01 in the F-test comparing the five 

means). 

Export-orientation and training 


their training in starting a business, Table 11.3. 

Trained entrepreneurs export more frequently 

than untrained entrepreneurs, as might be expected 

(the difference is statistically significant, 

with p-value 0.02 in the one-sided t-test comparing 

the two means). 

Table 11.4. Competent entrepreneurs appear 

slightly more export-oriented than entrepreneurs 

without this competency, but the small 

difference is only marginally significant (the 

probability-value is 0,07 in the one-sided t-test 

comparing the two averages). 

Export-orientation and networking 


their networking, Table 11.5. Entrepreneurs with 

extensive advisory networks tend to export 

more than entrepreneurs with few advisors, as 

might be expected. The correlation of exports 

with number of advisors is positive, 0.2 and is 

statistically significant (with probability-value 

0.005 in a one-sided test). 

Table 11.3 

Exporting by entrepreneurs, by their 


Denmark 2008 


Exporting 46 % 33 % 

Not exporting 54 % 67 % 

Sum 100 % 100 % 

Mean log export 1.4 0.8 


Table 11.5 


networking with advisors 

Denmark 2008 

Small Large 

network network 



Sum 100 % 100 % 



Export-orientation and competency 

Entrepreneurs’ export-orientation appears related 

to their competency to start a business, 

Table 11.4 


competency 



competent 



Sum 100 % 100 % 


N entrepeneurs 1.609 335 

The above cross-tabulations are limited because 

a cross-tabulation examines the relationship 

between exports and only one condition, 

while ignoring other conditions that may impact 

the relationship. Therefore, a more elaborate 

analysis will be undertaken next. 

Effects upon exports from education, 


The question now is, what are the distinct individual 

effects upon exporting from education, 

training, competency and networking, while 

holding other conditions constant, and how are 

these conditions jointly, in sequence, affecting 

exports? 

72

ed by regression (Table 11.6 in the appendix, 

with controls for other conditions). Exporting is 

greatly promoted by networking, and also promoted 

by training. Entrepreneurs with vocational 

education, or short or medium higher 

education, or without vocational or higher education, 

are less exporting than entrepreneurs 

with long higher education. In other words, entrepreneurs 

with long higher education are especially 

export-oriented. 


and networking were estimated in Chapter 

9 (Figure 9.2), except the effects of education 

upon networking. 

The effects leading to exports are shown in Figure 

11.2, where a positive effect is shown as a 

solid arrow, a negative effect is symbolized by a 

dashed arrow, and the magnitude of the effect 

is represented by the thickness of the arrow. 

Figure 11.2 

Estimated effects upon exports from education, training, competency and networking 



Medium higher 

education 

Competency 

Short higher 

education 

Vocational 

education 




Exports 

73

Chapter 12 

Entrepreneurs’ growth-expectations expanded by education, training, 


How are entrepreneurs’ expectations for growth 

formed by their learning? More specifically, this 

chapter addresses the questions, What are the 

separate effects upon growth-expectations from 

education, from training, from competency and 

from networking? 

The idea of effects upon entrepreneurs’ expectations 

from their education, training, competency 

and networking can be symbolized as a 

diagram with sequences of arrows, Figure 12.1. 

Each arrow here symbolizes a hypothesized effect. 

The effect may be positive or negative, and 

of large or small magnitude, or it might actually 

be nill. The task then is to estimate each distinct 



different from zero, positively or negatively, 


These effects shall be estimated in this chapter. 

Entrepreneurs’ education, training, competency 

and advisory network were examined in Chapter 

9, so here we focus on their expectations for 

growth. 

Growth here refers to the change in size of the 

firm, where size is the number of persons working 

for the firm. A startup venture has size 0, at 

its very start, but the starter may expect to get 

a number of people to work for the firm in the 

Figure 12.1 

Hypothesized effects upon growth-expectations from education, training, competency and 




Competency 


Growth-expectation 

74

future. To measure such expectations we ask 

the starters, 

How many people will be working for this 

business, not counting the owners but including 

all exclusive subcontractors, when it is five 

years old? 

An existing firm may already employ some people, 

and may expect positive or negative growth, 

that is, expansion, stability or contraction. The 

owner-managers are asked similarly, 

Five years from now how many people, not 

counting the owners but including all exclusive 

subcontractors, will be working for this 

business? 

To measure the change, we also ask the ownermanager 

about the current size, 

Right now how many people, not counting the 

owners but including exclusive subcontractors, 

are working for this business? 

The expected change in number of people working 

for the firm is then, 

Number expected in five years – Number 

now, 

where the ’Number now’ in a startup is 0 (if a 

respondent reports to be starting but also reports 

to have a staff, then I here classify the respondent 

as an owner-manager in an existing 

business). 

Expected change is often expressed alternatively 

as a ratio, the future relative to the present, 

(1 + Number expected in five years) / (1 + 

Number now) 

where 1 is added to represent the starter or 

owner. 

This expected change may also be expressed 

on a logarithmic scale as 

Logarithm [ (1 + Number expected in five 

years) / (1 + Number now) ], 

which may equally well be written as 

Logarithm [ 1 + Number expected in five years ] 

– Logarithm [ 1 + Number now ] 

which is the change in size, where size is measured 

on a logarithmic scale. This way of measuring 

expected change is especially suitable 

here (this avoids outliers, the few respondents 

reporting a huge change in number of employees, 

who would distort an analysis of number of 

employees). This logarithmic measure of expectation 

will be used extensively in tests in 

this chapter. 

Entrepreneurs differ considerably in their expectations, 

Table 12.1. Many entrepreneurs expect 

to expand by adding jobs, but many also 

expect no change in jobs. 

Expectations differ widely by phase. Most starters 

expect to expand, whereas most ownermanagers 

expect to have no change, i.e. neither 

expansion nor contraction. Because starters 

and owner-amanagers differ so widely in their 

expectations, they will be analyzed separately 

in the following. 

Table 12.1 

Expectations about growth 


Starters 


Expansion expected 75 % 35 % 

No change expected 25 % 58 % 

Contraction expected - 7 % 

Sum 100 % 100 % 

Additional jobs expected, median of numbers 3 0 

Additional jobs expected, mean of numbers 12.1 3.5 

Additional jobs expected, mean of logarithmic measure 1.5 0.2 

N respondents 383 1.550 

75

Table 12.2 

Growth-expectations of starters and owner-managers, by their education 


Starters 


Vocational Short Medium Long Neither Vocational Short Medium Long Neither 

education higher higher higher voc. nor education higher higher higher voc. nor 

education education education higher edu. education education education higher edu. 

Expansion expected 75% 77% 76% 70% 77% 33% 27% 33% 38% 40% 

No expansion expected 25% 23% 24% 30% 23% 67% 73% 67% 62% 60% 

Sum 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 

Mean expectation 1.5 1.6 1.7 1.5 1.4 0.1 0.2 0.2 0.3 0.2 

N entrepreneurs 106 39 95 91 48 488 115 308 416 219 

We shall now examine the relationship between 

expectation and education, then then relationship 

between expectation and training, then the 

relationship between expectation and competency, 

and then the relationship between 

expectation and networking, where each relationship 

will be examined without considering 

how the relationship may be impacted by the 

other conditions. This limitation will then be 

overcome by analyzing the effects of all four 

conditions simultaneously, estimating the effect 

of each while controling for the other conditions. 

Growth-expectations and education 

Are entrepreneurs’ expectations related to their 

education? Starters’ and and owner-managers’ 

expectations are shown in Table 12.2 for each 

educational level. Among the starters, there is 

no evidence that expectations differ among the 

educational groups. Among the owner-managers, 

expectations differ among the educational 

groups (the p-value is 0.05 for the chi-square 

test of the cross-tabulation, and the p-value is 

0.03 for the anova F-test of differences among 

the five means of the logarithmic measure). 

Owner-managers with long higher education 

expect most expansion, on average, and owner-managers 

with vocational education have 

the lowest average expectation. 

Growth-expectations and training 

Are entrepreneurs’ growth-expectations related 

to their training? Trained and untrained starters’ 

and owner-managers’ expectations are 

shown in Table 12.3. Apparently trained starters 

and owner-managers have higher expectations 

than untrained starters and owner-managers, 

but the samples are so small as to render 

the differences statistically insignificant. 

Table 12.3 

Growth-expectations of starters and owner-managers, by their training 

Denmark 2008 

Starters 


Trained Untrained Trained Untrained 

Expansion expected 100 % 88 % 41 % 29 % 

No expansion expected 0 % 12 % 59 % 71 % 

Sum 100 % 100 % 100 % 100 % 

Mean expectation 1.8 1.7 0.3 0.2 

N entrepreneurs 9 25 27 83 

76

Growth-expectations and competency 

Entrepreneurs’ growth-expectations are related 

to their competency to start a business, Table 

12.4. Competent starters expect more expansion 

than starters without this competency (the p- 

values are less than 0.003 in both the one-sided 

chi-square test and the one-sided t-test). Competent 

owner-managers expect greater expansion 

than less competent owner-managers (the 

p-values are less than 0.0001 in both the onesided 

chi-square test and the one-sided t-test). 

Growth-expectations and networking 

Are entrepreneurs growth-expectations related 

to their networking with advisors? For starters, 

there is no evidence of an association. For owner-managers, 

the growth-expectation is higher 

for those with a large network than for those 

with a small network (the p-value is 0.02 for the 

one-sided t-test comparing the two means). 

The above cross-tabulations of expectation related 

to each condition are limited because they 

ignore other conditions that may impact the relation. 

Therefore, more elaborate analysis will 

be undertaken next. 

Effects upon growth-expectations from 



The question now is: how is the expectation 

shaped by distinct effects from each condition, 

while holding the other conditions constant? 

The effects upon growth-expectations from 

education, training, competency and networking 

are estimated by regression (Table 12.6 in 

the Appendix). The effects for starters are rather 

uncertain, partly because the sample is rather 

small. The effects for owner-managers are 

more certain. Owner-managers’ growth-expectations 

are promoted considerably by their 

competency, by their networking and apparently 

also by their training. Owner-managers 

with vocational education, or short or medium 

higher education, or without vocational or high- 

Table 12.4 

Growth-expectations of starters and owner-managers, by their competency 


Starters 


Competent Less competent Competent Less competent 



Sum 100 % 100 % 100 % 100 % 


N entrepeneurs 316 50 1.245 272 

Table 12.5 

Growth-expectations of starters and owner-managers, by their networking with advisors 

Denmark 2008 

Starters 


Small network Large network Small network Large network 



Sum 100 % 100 % 100 % 100 % 


N entrepreneurs 14 20 61 49 

77

er education have lower expectations than 

those with long higher education. In other 

words, those with long higher education have 

especially high expectations for growth. 


and networking with advisors were estimated 

in Chapter 9 (Figure 9.2), except the effects 

of education upon networking. 

These estimated effects upon owner-managers’ 

growth-expectations are shown in Figure 12.2, 

where a positive effect is drawn as a solid arrow, 

a negative effect is denoted by a dashed 

arrow, and the magnitude of an effect is shown 

by the thickness of the arrow. 

Figure 12.2 

Estimated effects upon growth-expectations from education, training, competency and 


Owner-managers in Denmark 


Medium higher 

education 

Competency 

Short higher 

education 

Vocational 

education 




Growth 

expectations 

78

Part 5 

CONCLUSIONS 

Chapter 13 

Expanding entrepreneurship through education, training, competency and 


The analyses in the above chapters were undertaken 

to address the major question: How is entrepreneurship 

expanding through learning? This 

chapter draws conclusions to the two sides of 

this question: How are people expanding their 

involvement in entrepreneurship? How are entrepreneurs 

expanding their innovation, exports 

and growth-expectations? Before address ing 

these issues, we draw some conclusions about 

entrepreneurship in society, specifically in Denmark. 

Entrepreneurship in society 


Denmark? More specifically, Chapter 3 addressed 

the issues, Is the trend upward, stable 

or downward? How does the level in Denmark 

compare to the level in other societies? 

The level of early-phase entrepreneurial activity, 

as a rate or prevalence in the adult population 

in Denmark, is today about the typical for 

developed countries. Indeed, for several years, 

Denmark has been in the middle among the developed 

countries. However, when measuring 

the earlier-phase of prospecting, then we observe 

a decline in the rate of adults who intend 

to start a new firm within a few years. This decline 

in the intentions in the population should 

be taken serious as an early warning of an impending 

decline in the rate of starting new businesses. 

The observed decline in people’s intentions 

even precede the economic crisis that 

came in 2008. 

How are the national conditions for entrepreneurship 

in Denmark? More specifically, Chapter 

4 answered the questions, Are cultural and 

institutional conditons in Denmark improving or 

deteriorating? How does the framework in Denmark 

compare to the framework in other societies? 

In recent years most of the framework conditions 

have tended to become more favorable 

and are now more favorable today than they 

were a few years ago (perhaps except in 2008 

when the economic crisis hit). A few of the 

framework conditions seem to be stable, without 

becoming more favorable or becoming less 

favorable. None of the framework conditions 

are seriously worse today than they were a few 

years earlier. So, on the whole, the framework 

for entrepreneurship is more favorable today 

than a few years ago. 

Despite the improvements in the framework 

conditions, most of the Danish framework conditions 

are rather close to the typical situation 

for developed countries, and rank around the 

middle among the developed countries. A few 

conditions are much more favorable in Denmark 

than typical among the developed countries. 

Conversely, a few conditions are much 

less favorable in Denmark than typical among 

the developed countries. So, on the whole, the 

framework in Denmark is about as favorable as 

the typical for the developed societies. 

79


More specifically, Chapter 5 addressed 

the questions, How are the cultural conditions 

affecting one another? How are the institutional 

conditions interrelated? How are the cultural 

and institutional conditions affecting entrepreneurship? 

A culture of individualism in a typical developed 

society promotes the esteem of the entrepreneurial 

vocation, enhances the entrepreneurial 

education and increases the entrepreneurial 

skills in the population. Education also has a 

distinct positive effect on the skills in the population. 

The basic institutional framework conditions 

create opportunities for the people to 

bring their skills into entrepreneurship. Opportunities 

coupled with skills jointly tend to promote 

the level of entrepreneurship in the developed 

society. 

This dynamic between framework conditions 

and level of entrepreneurship is illustrated by 

Denmark’s position. Denmark has a level of entrepreneurship 

that is about typical for the developed 

countries and a framework that is about 

typical. This is easily understood. When the 

country has a framework that is typical, then we 

also predict the resulting level of activity to be 

typical. 

The tight coupling between framework and performance 

is also exemplified by the USA. The 

USA has the best framework overall. This framework 

implies that the entrepreneurial performance 

in the USA is very high, indeed, the USlevel 

is among the highest among the developed 

societies. 

People involved in entrepreneurship through 




Denmark and other societies More specifically, 

Chapter 6 addressed the problems, How much 

of the adult population is trained? Have people 

been trained mainly during their schooling 

or later in life? What is the organizational context 

of later training, and is this voluntary or 

compulsory? How is people’s training affected 

by their gender, age and education? Is the training 

expanding in Denmark and in other societies? 

About 22% of the adults in Denmark have received 

training in starting a business, less frequently 

during their schooling, and then is was 

typically compulsory rather than voluntary, 

more frequently later than their schooling. The 

later trained people have received their training, 

typically voluntarily, in a variety of organizational 

contexts, often during further formal 

education, and often informally. People’s training 

is affected by gender, age and education in 

the ways that women are less frequently trained 

than men, older adults far less frequently than 

young adults, and people with long higher education 

less frequently than others. That training 

is more typical in the young generation than in 

the older generation is indicative of the current 

expansion of training. The expansion in Denmark 

seems to be faster than the expansions in 

most other developed societies. 


only through training, but also through their education, 

competency and networking? More specifically, 

Chapter 7 dealt with the issues, Are people 

in Denmark educated at high and diverse 

levels in the educational system? Are people 

typcially competent to start a business? Is 

people’s competency enhanced by their education 

and training? Are people typically networking 

with starters? Is people’s networking promoted 

by their education, training and competency? 

Many people in Denmark have a vocational education, 

but more have a short, medium or long 

further education,while fewer have neither vocational 

nor higher education. Education affects 

training in the manner that people with long 

higher education are less likely than others to 

be trained for entrepreneurship, as mentioned 

above. People’s education and training have 

distinct effects upon their competency to start 

a business, in the ways that long higher education 

promotes competency, and training also 

enhances competency. These have further dis- 

80

Figure 13.1 

People’s vocation affected by their education, training, competency and networking. 


Vocation 


Competency 



tinct effects upon networking, knowing a starter. 

People are especially likely to know a starter 

if they are educated, trained and competent. 

These effects are pictured in Figure 13.1 (with 

details in Figures 6.2 and 7.2), where a positive 

effect is shown as a solid arrow, a negative effect 

is displayed as a dashed arrow, and the 

magnitude of the effect is indicated by the thickness 

of the arrow. 


shaped by their learning? More specifically, 

Chapter 8 addressed the questions, How is people’s 

vocation – entrepreneur or not – affected 

by their education, by their training, by their 

competency and by their networking? 

People’s education, training, competency and 

networking have distinct effects upon their vocation, 

whether they are entrepreneurs (prospective 

starters, starters or owner-managers). 

People with long higher education are especially 

likely to be entrepreneurs (of any of the three 

kinds). Trained people are more likely than untrained 

people to be entrepreneurs, competent 

people are more likely than less competent people 

to be entrepreneurs, and people who network 

are more likely to be entrepreneurs than 

people who do not network with a starter. That 

training, competency and networking enhance 

likelihood of being entrepreneurs may not be so 

surprising, but it is important that each makes a 

distinct contribution to expanding people’s in- 

81

volvement in entrepreneurship. These effects 

upon people’s vocation are illustrated in Figure 

13.1 (with details in Figure 8.2). 

Entrepreneurs expanding innovation, 

exporting and growth-expectations 

How are Danish entrepreneurs learning? More 

specifically, Chapter 9 addressed the questions, 

Are entrepreneurs educated at different levels 

than other people? Are entrepreneurs typically 

trained to start a business, whereas other people 

are typically untrained? Are entrepreneurs 

typically competent to start a business, whereas 

other people typically do not have this competency? 

Are entrepreneurs networking with a 

wide spectrum of advisors? What are the effects 

among entrepreneurs’ education, training, competency 


Entrepreneurs in Denmark more frequently 

have a long higher education than non-entrepreneurs, 

and less frequently than non-entrepreneurs 

have neither vocational nor higher 

education. Entrepreneurs are typically not 

trained to start a business, although they more 

often are trained than non-entrepreneurs, of 

course. Non-entrepreneurs are typically not 

trained to start a business, but many non-entrepreneurs 

actually have some training. This indicates 

that there is a trained segment in the population 

for mobilization for entrepreneurship. 

How is entrepreneurs’ innovativeness expanded 

by their learning? More specifically, Chapter 10 

addressed the questions, What are the distinct 

effects upon innovativeness from education, 


Innovativeness of entrepreneurs (here starters 

and owner-managers in Denmark) is promoted 

by distinct effects from their education, training, 

competency and network in the ways that 

innovativeness is especially expansive by entrepreneurs 

with long higher education, with 

training, with competency and with extensive 

networks of advisors. 

How is entrepreneurs’ exporting expanded by 

their learning? More specifically, Chapter 11 addresses 

the questions, What are the separate 

effects upon export-orientation from education, 


Export-orientation of entrepreneurs (here starters 

and owner-managers) is enhanced by separate 

effects from their education, training, competency 

and network in the ways that the export-orientation 

is especially expansive by entrepreneurs 

with long higher education, with 

training, with competency and with extensive 

networks of advisors. 

How are entrepreneurs’ expectations for growth 

formed by their learning? More specifically, 

Chapter 12 addresses the questions, What are 

the separate effects upon growth-expectations 

from education, from training, from competency 

and from networking? 

Growth-expectations held by entrepreneurs 

(here starters and owner-managers) are expanded 

by separate effects from their education, 

training, competency and network in the 

ways that the growth-expectations are especially 

expansive by entrepreneurs with long 

higher education, with training, with competency 

and with extensive networks of advisors. 

Expansive entrepreneurship shall here denote 

entrepreneurship which is innovative, exporting 

and growth-expecting. We will thus call entrepreneurship 

expansive to the degree it is innovative, 

exporting and expecting growth. 

Likewise, an entrepreneur is expansive to the 

extent the entrepreneur is innovative, exporting 

and expecting growth. 

Expansiveness can be measured as an index 

combining the measures of innovation, exports 

and growth-expectation (for the starters, the 

three measures are standardized and then averaged; 

for the owner-managers, the three measures 

are also standardized and then averaged). 

It is reasonable to combine the three measures 

because they are positively correlated with one 

another. 

82

Figure 13.2 

Entrepreneurs’ expansiveness in innovation, exporting and growth-expectations, 

affected by their education, training, competency and networking 


Expansiveness 


Competency 



Expansiveness of entrepreneurs (starters and 

owner-managers in Denmark) is shaped by separate 

effects from their education, training, 

competency and networking. The effects are 

estimated by regression (Table 13.1 in the Appendix). 

Expansiveness is affected by education 

in the way that entrepreneurs with long 

higher education are especially expansive. Entrepreneurs 

with training, with competency and 

with extensive networks are also especially expansive. 

13.1, all in the Appendix), where a positive effect 

is shown by a solid arrow, a negative effect 

by a dashed arrow, and the magnitude of the 

effect is indicated by the thickness of the arrow 

These effects on expansiveness are pictured in 

Figure 13.2 (based on Tables 9.12, 9.13, 9.14 and 

83

Appendix with technical specifications 

This appendix provides technical notes to the 

preceding chapters and presents the statistical 

models on which figures are based. 

Notes to the Danish summary, 

Sammenfatning 

Figur A in the Danish summary, Sammenfatning, 

is based on Figure 13.1 in Chapter 13. 

Figur B in the Danish summary, Sammenfatning, 

is based on Figure 13.2 in Chapter 13. 

Notes to Chapter 2 

The 39 developed societies are a relatively large 

sample of developed societies, the sample is 

nearly a census of all the developed societies in 

the world (the developed societies that have 

not yet participated in GEM are mainly new 

states in Eastern Europe such as Slovakia, Estonia, 

Lithuania, Ukraine and Belarus). 


Figure 5.1 in Chapter 5 is based on the regression 

shown here in Table 5.2. The unit of analysis 

is a developed society. Data are available on 

the 39 developed societies participating in GEM 

in 2002-08 (listed in Chapter 2). 

Figure 5.2 in Chapter 5 is based on the regressions 

shown here in Tables 5.3, 5.4 and 5.5. The 

unit of analysis is a developed society. Data are 

available on all 39 developed societies participating 

in GEM during some or all of the years 

2002-08 (listed in Chapter 2). 

Figure 5.3 in Chapter 5 is based on the regres- 

Table 5.2 

Linear regression of opportunities upon institutional conditions. 

The other institutional conditions had no discernible distinct effects. 

Standardized regression 

Probability-value 

coefficient 

Financial resources 0.07 0,35 one-tailed 

Government policies 0.16 0,18 one-tailed 

Commercial and legal infrastructure 0.32 0,04 one-tailed 

Internal market openness 0.32 0,01 one-tailed 

Intellectual property rights 0.21 0,14 one-tailed 

R 2 = 0.56 

N = 39 developed societies 

Table 5.3 

Linear regression of esteem upon individualism. 



coefficient 

Individualism 0.86 0.0001 one-tailed 

R 2 = 0.73 

N =39 developed societies 

85

Table 5.4 

Linear regression of nations’ education upon individualism (esteem had no 

discernible effect). 



coefficient 


R 2 = 0.31 


Table 5.5 

Linear regression of nations’ skills upon education and individualism (esteem had 

no discernible effect). 



coefficient 

Education 0.15 0.06 one-tailed 


R 2 = 0.76 


Table 5.7 

Linear regression of nations’ entrepreneurship upon opportunities and skills 

(education, esteem and individualism had no discernible distinct effects). 

Regression Standardized Propabilitycoefficient 

regression coefficient value 

Opportunities 4.43 0.39 0.02 one-tailed 

Skills 1.02 0.13 0.24 one-tailed 

Constant - 10.41 

R 2 = 0.22 


sion shown here in Table 5.7. The unit of analysis 

is a developed nation. Data are available on 

all 39 developed nations participating in GEM 

during some or all of the years 2002-08 (listed in 

Chapter 2). Entrepreneurial activity is measured 

by TEA (Chapter 3), with each country’s mean 

across the years 2002-08. Opportunities, and 

also skills, is measured as the country’s mean 

across the years 2002-08. 

axis refers to entrepreneurial activity, as measured 

by TEA (each country’s mean across the 

years 2002-08). The horizontal axis refers to the 

level of TEA predicted by the expression: 

Predicted TEA = – 10.41 + 4.43 * Opportunities + 

1.02 * Skills 

using the regression coefficients from Table 5.7 

Figure 5.4 in Chapter 5 plots the developed nations 

according to the predicted TEA in Table 

5.7 and the TEA. More precisely, the vertical 

86

Table 6.9 

Logistic regression of adults’ training 


Coefficient Odds ratio Probability-value 

Medium higher education 0.3 1.3 0.11 two-tailed 

Short higher education 0.7 1.9 0.003 two-tailed 

Vocational education 0.5 1.7 0.003 two-tailed 

Neither higher nor voc. ed. - 0.1 0.9 0.52 two-tailed 

Gender (female 0, male 1) 0.3 1.4 0.001 one-tailed 

Age (logarithmic) - 1.5 0.2 0.0001 one-tailed 

Constant 3.8 



Figure 6.2 in Chapter 6 is based on the regression 

shown here in Table 6.9. The unit of analysis 

is an adult in Denmark. Data are available on 

almost all 2012 adults participating in our survey 

in 2008. The questions about training were 

not asked in previous years. 

Figure 7.2 in Chapter 7 is based on Figure 6.2 

and on the regressions shown here in Tables 7.8 

and 7.9. The unit of analysis is an adult in Denmark. 

The sample of respondents approximates 

a simple random sample of the adult population 

in Denmark and is thereby fairly representative 

of the population of adults (ages 18 to 64). Data 

for these analysis are available on by far most 

adults participating in our surveys in the years 

2003-08, except that the questions about training 

were asked only in 2008. Therefore I regressed 

on the independendent variables including 

training using only the data for 2008 

and I regressed on the independent variables 

excluding training using the data for 2003-08. 

Table 7.8 

Logistic regression of adults’ competency 



Medium higher education - 0.3 0.7 0,0001 one-tailed 

Short higher education - 0.4 0.7 0,0001 one-tailed 

Vocational education - 0.3 0.7 0,0001 one-tailed 

Neither higher nor voc. ed. - 0.8 0.4 0,0001 one-tailed 

Gender (female 0, male 1) 1.0 2.6 0,0001 one-tailed 

Age (logarithmic) 0.5 1.6 0,0001 one-tailed 


Training * 0.9 2.4 0,0001 one-tailed 

N = 17.686 adults 

* Estimated by 2008 data 

87

Table 7.9 

Logistic regression of adults’ networking 



Medium higher education - 0.2 0.8 0.0001 one-tailed 

Short higher education - 0.4 0.7 0.0001 one-tailed 

Vocational education - 0.4 0.7 0.0001 one-tailed 

Neither higher nor voc. ed. - 0.7 0.5 0.0001 one-tailed 

Competency 0.8 2.2 0.0001 one-tailed 



Constant 4.3 

Training * 0.2 1.2 0.06 one-tailed 

N = 17.534 adults 


Tables 7.8 and 7.9 show the regression estimates 

using the data for 2003-08 and also the 

estimated effect of training using the data from 

2008 (with 2012 surveyed adults), as marked 

with an asterix. 


Figure 8.2 in Chapter 8 is based on Figure 7.2 

and on the regression shown here in Table 8.6. 

The unit of analysis is an adult in Denmark. Data 

are available on by far most adults participating 

in our surveys in the years 2003-08, except that 

the questions about training were asked only in 

2008. Therefore I regressed on the independendent 

variables including training using only 

the data for 2008 and I regressed on the independent 

variables excluding training using the 

data for 2003-08. Tables 7.8 and 7.9 show the 

Table 8.6 

Logistic regression of adults’ vocation, entrepreneur contrasted lay 

Adults (all kinds) in Denmark 2003-08 


Medium higher education - 0.4 0.7 0.0001 one-tailed 

Short higher education - 0.1 0.9 0.12 one-tailed 

Vocational education - 0.3 0.7 0.0001 one-tailed 

Neither higher nor voc. ed. - 0.4 0.7 0.0001 one-tailed 

Competency 1.7 5.3 0.0001 one-tailed 

Networking with starter 0.9 2.6 0.0001 one-tailed 




Training * 0.1 1.1 0.30 one-tailed 

N = 17.534 adults 


88

Table 8.7 

Logistic regression of adults’ vocation, prospective starter 

contrasted non-entrepreneurs 

Adults (only non-entrepreneurs and prospective starters) in Denmark 2003-08 


Medium higher education 0,0 1,0 

Short higher education 0,1 1,1 0,61 two-tailed 

Vocational education - 0,1 0,9 0,25 two-tailed 

Neither higher nor voc. ed. - 0,2 0,8 0,02 two-tailed 

Competency 1,6 4,7 0,0001 one-tailed 

Networking with starter 0,9 2,5 0,0001 one-tailed 

Gender (female 0, male 1) 0,2 1,2 0,0002 one-tailed 

Age (logarithmic) - 1,4 0,2 0,0001 one-tailed 

Constant 1,3 

Training * 0,3 1,3 0,08 one-tailed 

N = 16.889 adults 


regression estimates using the data for 2003-08 

and also the estimated effect of training using 

the data from 2008 (with 2012 surveyed adults), 

as marked with an asterix. 

The regression in Table 8.6 examines vocation 

by contrasting entrepreneurs with non-entrepreneurs. 

Some independent variables are not 

always causally prior to vocation, but are often 

effects of vocation, notably does being an entrepreneur 

such as an owner-manager often affect 

competency and networking. To better estimate 

the effects of competency, networking 

and the other independent variables upon vocational 

choice; we can contrast the non-entrepreneurs 

with entrepreneurs in the earliest 

phase, the prospective starters, as here in Table 

8.7. The results are fairly similar to those in Table 

8.6, notably both competency and networking, 

have positive effects. Table 8.7 is not used 

Table 8.8 

Logistic regression of adults’ vocation, starter contrasted non-entrepreneur 

Adults (only non-entrepreneurs and starters) in Denmark 2003-08 


Medium higher education - 0,1 0,9 0,52 two-tailed 








Constant - 3,0 


N = 17.514 adults 


89

Table 8.9 

Logistic regression of adults’ vocation, owner-manager contrasted non-entrepreneur 

Adults (only non-entrepreneurs and owner-managers) in Denmark 2003-08 


Medium higher education - 0,3 0,7 0,0001 two-tailed 

Short higher education - 0,1 0,9 0,20 two-tailed 






Age (logarithmic) 0,8 2,1 0,0001 one-tailed 

Constant - 6,6 

Training * - 0,2 0,9 0,23 one-tailed 

N = 17529 adults 


for any figure in the monograph, but is included 

here to show robustness of results. 

The robustness of estimated effects can also 

be examined by focusing on vocation in a contrast 

between non-entrepreneurs and starters, 

as here in Table 8.8. The results are similar. Table 

8.8 is not used for any figure in the monograph, 

but is included here to show robustness 

of results. 

Effects are also estimated for vocation as a contrast 

between non-entrepreneurs and ownermanagers, 

as here in Table 8.9. The coefficients 

are fairly similar to those in the regresssions us- 

ing staters or prospective starters (the estimated 

coefficient for training is here negative, but 

quite insignificant). Table 8.9 is not used for any 

figure in the monograph, but is included here to 

show robustness of results. 


Figure 9.2 in Chapter 9 is based on Tables 9.11, 

9.12 and 9.13. The unit of analysis is an entrepreneur. 

The sample is the entrepreneurs identified 

in our surveys of adults in Denmark. The sample 

of entrepreneurs is fairly representative of 

the population of entrepreneurs in Denmark, 

because the sampled adults are a fairly representative 

sample of the population of adults in 

Table 9.11 

Logistic regression of entrepreneurs’ training 



Medium higher education 0,5 1,7 0,17 two-tailed 


Vocational education 0,5 1,7 0,19 two-tailed 

Neither vocational nor hi ed 0,4 1,5 0,33 two-tailed 



Constant 2,1 

N = 305 entrepreneurs 

90

Table 9.12 

Logistic regression of entrepreneurs’ competency 

Entrepreneurs in Denmark 2003-08 


Medium higher education - 0,1 0,9 0,23 one-tailed 

Short higher education - 0,3 0,8 0,04 one-tailed 

Vocational education 0,0 1,0 

Neither vocational nor hi ed - 0,3 0,7 0,005 one-tailed 



Constant -1,7 


N = 3.386 entrepreneurs 

* Estimates from 2008 data 

Table 9.13 

Linear regression of entrepreneurs’ network size 

Starters and owner-managers in Denmark 2008 


Medium higher education 0,5 0,05 0,53 two-tailed 

Short higher education - 0,6 - 0,05 0,54 two-tailed 

Vocational education 0,4 0,04 0,59 two-tailed 

Neither vocational nor hi ed 1,2 0,09 0,22 two-tailed 

Training 1,7 0,18 0,003 one-tailed 


Gender (female 0, male 1) - 0,1 - 0,01 0,90 two-tailed 

Age (logarithmic) - 3,4 - 0,23 0,0004 one-tailed 

Constant 17,7 

R 2 = 0,14 

N = 217 

Denmark. Table 9.11 uses data for almost all entrepreneurs 

who participated in our survey in 

2008 when training was first measured (the 

sample of entrepreneurs is here small). 

Table 9.12 is based on the data on the entrepreneurs 

surveyed 2003-08. Data for these analysis 

are available on by far most entrepreneurs 

identified in our surveys in the years 2003-08, 

except that the questions about training and 

network size were asked only in 2008. Therefore 

I regressed on the independendent variables 

including training and networking using 

only the data for 2008 and I regressed on the 

independent variables excluding training and 

networking using the data for 2003-08. Table 

9.12 shows the regression estimates using the 

data for 2003-08 and also the estimated effect 

of training using the data from 2008, as marked 

with an asterix. 

Table 9.13 is based only on the starters and 

owner-managers identified in our survey in 2008, 

because these are the only ones who were 

asked about their network of advisors. The sample 

is small, only 217 entrepreneurs, and we are 

trying to estimate effects from a handful of educational 

groups, so the sample in each group is 

91

very small. The estimated effects are therefore 

not very precise and trustworthy (that is, 

the coefficients are not statistically significant). 

Effects from education upon networking 

are therefore not shown in Figure 9.2 in 

Chapter 9. 


Figure 10.2 is based on Table 10.5. The unit of 

analysis an entrepreneur who is a starter or 

owner-manager in Denmark. The sample is 

those identified in our surveys of adults in Denmark 

2003-08. I regressed innovativeness on 

the independent variables except training and 

network size using the data from 2003-08 and I 

regressed on the independent variables including 

training and network size from 2008 (because 

training and network size was measured 

only in 2008, when the sample of starters and 

owner-managers was small). The regression 

using the data from 2003-08 is shown in Table 

10.5. The estimated effects of training and network 

size using the data from 2008 are also 

shown, marked by an asterix. 

Table 10.5 

Linear regression of starters and owner-managers’ innovativeness 



coefficient value 

Medium - 0,02 - 0,02 0,27 one-tailed 

Short - 0,1 - 0,05 0,02 one-tailed 

Vocational - 0,1 - 0,11 0,0001 one-tailed 

Neither - 0,1 - 0,05 0,02 one-tailed 



Gender (female 0, male 1) - 0,1 - 0,05 None 

Phase (start 1, own-manage 0) 0,1 0,12 None 

Phase*Gender 0,1 0,09 0,02 two-tailed 

Firm age (logarithmic) - 0,1 - 0,16 0,0001 two-tailed 

Constant 1,6 


Network size * 0,01 0,11 0,09 one-tailed 

R 2 = 0,11 

N = 1.899 


92

Table 11.6 

Linear regression of starters’ and owner-managers’ exports (logarithmic) 



coefficient value 

Medium higher education - 0,2 - 0,05 0,04 one-tailed 

Short higher education - 0,4 - 0,07 0,004 one-tailed 

Vocational education - 0,4 - 0,10 0,0002 one-tailed 

Neither hi nor vocational educ - 0,2 - 0,04 0,06 one-tailed 

Competency 0,0 0,00 

Age (logarithmic) - 0,1 - 0,01 0,36 one-tailed 


Phase (start 1, own-manage 0) 0,2 0,06 0,03 two-tailed 

Firm age (logarithmic) 0,1 0,05 0,06 two-tailed 

Constant 1,4 


Network size * 0,1 0,20 0,02 one-tailed 

R 2 = 0,02 

N = 1.818 



Figure 11.2 is based on Table 11.6. The unit of 

analysis an entrepreneur who is a starter or 

owner-manager in Denmark. The sample is 

those identified in our surveys of adults in Denmark 

2003-08. I regressed exports (measured 

on a logarithmic scale, as log(1 + percent customers 

abroad) to avoid skewness and outliers) 

on the independent variables except training 

and network size using the data from 2003-08 

and I regressed on the independent variables 

including training and network size from 2008 

(because training and network size was measured 

only in 2008, when the sample of starters 

and owner-managers was small). The regression 

using the data from 2003-08 is shown in 

Table 11.6. The estimated effects of training and 

network size using the data from 2008 are also 

shown, marked by an asterix. 

93

Table 12.6 

Linear regression of growth-expectations of starters and owner-managers 


Starters 


Standardized Probability- Standardized Probabilitycoefficient 

value coefficient value 

Medium higher education 0,10 0,12 two-tailed - 0,05 0,03 one-tailed 

Short higher education 0,04 0,55 two-tailed - 0,05 0,03 one-tailed 

Vocational education 0,0 - 0,09 0,002 one-tailed 

Neither voc nor hi ed 0,0 - 0,07 0,01 one-tailed 

Competency 0,12 0,01 one-tailed 0,08 0,001 one-tailed 

Age (logarithmic) - 0,02 0,39 one-tailed - 0,08 0,002 one-tailed 

Gender(female 0, male 1) 0,16 0,001 one-tailed 0,07 0,003 one-tailed 

Firm age (logarithmic) 

- 0,23 0,0001 one-tailed 

Training * 0,05 0,42 one-tailed 0,01 0,48 one-tailed 

Network size * 0,24 0,18 one-tailed 0,06 0,27 one-tailed 

R 2 0,05 0,09 

N 361 1.448 

* Estimated on 2008 data 


Figure 12.2 is based on the regression of ownermanagers 

in Table 12.6. For the regression in 

the left side of Table 12.6, the unit of analysis is 

a starter, and the sample is the starters identified 

in our surveys of adults in 2003-08. For the 

regression on the right side of Table 12.6, the 

unit of analysis is an owner-manager, and the 

sample is the owner-managers identified in our 

surveys of adults in 2003-08. I analyze starters 

and owner-managers separately because they 

differ greatly in their growth-expectations, the 

dependent variable. I regressed on the independent 

variables except training and network 

size using the data from 2003-08, and I regressed 

on the independent variables including 

training and network size from 2008 (because 

training and network size was measured only in 

2008, when the samples of starters and ownermanagers 

were small, so small that the coefficients 

could hardly be statistically significant). 

The regression using the data from 2003-08 is 

shown in Table 12.6. The estimated effects of 

training and network size using the data from 

2008 are also shown, marked by an asterix. 

94

Table 13.1 

Linear regression of starters’ and owner-managers’ expansiveness (combined 

innovation, exporting and growth-expectation) 


Standardized coefficient 


Medium higher education - 0,08 0,004 one-tailed 

Short higher education - 0,09 0,0004 one-tailed 

Vocational education - 0,16 0,0001 one-tailed 

Neither hi nor vocational educ - 0,10 0,0003 one-tailed 

Competency 0,06 0,005 one-tailed 

Age (logarithmic) - 0,05 0,04 one-tailed 

Gender (female 0, male 1) 0,06 0,01 one-tailed 

Phase (start 1, own-manager 0) 0,04 0,14 two-tailed 

Firm age (logarithmic) - 0,15 0,0001 one-tailed 

Training * 0,08 0,18 one-tailed 

Network size * 0,15 0,06 one-tailed 

R 2 = 0,06 

N = 1.617 



Figure 13.1 in Chapter 13, Conclusion, is based 

on Table 6.9, 7.8, 7.9 and 8.6 in this appendix. 

Figure 13.2 in Chapter 13 is based on Tables 

9.12, 9.13 and 9.14 earlier in this appendix and 

Table 13.1 here. 

95

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101

102

National Teams in 2008 

in Global Entrepreneurship Monitor 

Team Institution National Team 

Members 

Financial Sponsors 

APS Vendor 

Angola 

Universidade 

Católica de Angola (UCAN) 

Sociedade 

Portuguesa de 

Inovação (SPI) 

Alves da Rocha 

Salim Abdul 

Valimamade 

Augusto Medina 

Sara Medina 

Douglas Thompson 

Anders Hyttel 

João Medina 

Banco de Fomento S.A. 

Centro de 

Pesquisas, 

Sondagens e 

Estudos de Opinião 

(CENSOP 

– Dr. Bernardo 

Vieira) 

Argentina 

Center for 


IAE Management and 

Business School 

Universidad Austral 

Silvia Torres 

Carbonell 

Leticia Arcucci 

Hector Rocha 

Juan Martin 

Rodriguez 

Center for Entrepreneurship, 

IAE Management and Business School, 

Universidad Austral 

Banco Santander Rio 

Subsecretaría de Desarrollo Económico, 

Ministerio de 

Desarrollo Económico - Gobierno de la 

Ciudad de Buenos Aires 

MORI 

Argentina 

Prosperar, Agencia Nacional de Desarrollo 

de Inversiones 

Belgium 

Vlerick Leuven Gent 

Management School 

Hans Crijns 

Miguel Meuleman 

Olivier Tilleuil 

Flemisch Government, Steunpunt 

Ondernemen en Internationaal 

Ondernemen (STOIO) 

TNS Dimarso 

Bolivia 

Maestrias para el Desarrollo 

- Universidad Catolica 

Boliviana 

Marco Antonio Fernandez 

Gover Barja 

Mario Avila 

Fundación Nuevo Norte 

USAID/Bolivia 

Fundacion Avina 

Red Bolivia Emprendedora 

Fundación para la Producción 

Cima Group/ 

Synovate 

Bosnia and 

Herzegovina 


Development Center from 

Tuzla in partnership with 

Tuzla University 

Bahrija Umihanic 

Admir Nukovic 

Boris Curkovic 

Esmir Spahic 

Rasim Tulumovic 

Senad Fazlovic 

Sladjana Simic 

Entrepreneurship Development Center 

Tuzla 

Government of Tuzla Canton 

City of Tuzla 

Government of Brcko District of Bosnia and 

Herzegovina 

PULS BH d.o.o. 

103


Members 


APS Vendor 

Brazil 

IBQP - Instituto 

Brasileiro da 

Qualidade e 

Produtividade 

Simara Maria S. S. Greco 

Paulo Alberto Bastos Junior 

Joana Paula 

Machado 

Rodrigo G. M. 

Silvestre 

Carlos Artur Krüger Passos 

Júlio César Felix 

Marcos Mueller Schlemm 

Instituto Brasileiro da Qualidade e 

Produtividade – IBQP 

Serviço Brasileiro de Apoio às Micro e 

Pequenas Empresas – SEBRAE 

Serviço Nacional de 

Aprendizagem Industrial – SENAI / PR 

Serviço Social da Indústria - SESI / PR 

Bonilha 

Comunicação e 

Marketing S/C Ltda. 

Universidade Positivo 

Chile 

Universidad del 

Desarrollo 

José Ernesto Amorós 

Massiel Guerra 

InnovaChile de CORFO 

Opina S.A. 

Regional Teams: 

Antofagasta 

Coquimbo 

Valparaíso 

Bío-Bío 

Araucanía 

Universidad Adolfo Ibáñez 

Univ. Católica del Norte 

Univ. Católica del Norte 

Univ. Técnica 

Federico Santa María 

Univ. del Desarrollo 

Univ. de la Frontera 

-INCUBATEC 

Miguel Carrillo 

Bárbara Harris 

Gianni Romaní 

Miguel Atienza 

Karla Soria 

Cristóbal Fernández Robin 

Juan Tapia Gertosio 

Jorge Cea Valencia 

Olga Pizarro Stiepovic 

José Ernesto Amorós 

Carlos Isaacs 

Bornand 

Claudina Uribe Bórquez 

Franklin Valdebenito Godoy 

Gerardo Lagos Wietsenfeld 

Pedro Araneda Reyes 

Universidad Católica del Norte, DGIP. 

Gobierno Regional, 

Agencia Regional Desarrollo 

Productivo. 

Universidad Católica del Norte, DGIP. 

Gobierno Regional, 

Agencia Regional Desarrollo 

Productivo. 

Departamento de Industrias 

Y Centro de Ingeniería de 

Mercados, CIMER, de la Univ. Técnica 

Federico Santa María 

El Mercurio de Valparaíso 

UDD-Facultad de Economía y 

Negocios. 

Dirección de Innovación y 

Transferencia Tecnológica de la Universidad 

de La Frontera 

Colombia 

Universidad de los Andes 

Universidad ICESI 

Rafael Vesga 

Lina Devis 

Rodrigo Varela 

Luis Miguel Alvarez 

SENA 

Comfenalco Valle 

Centro 

Nacional de 

Consultoría 

Universidad del Norte 

Liyis Gomez 

Pontificia Universidad 

Javeriana Cali 

Fernando Pereira 

Raúl Quiroga 

Alberto Arias 

Croatia 

J.J. Strossmayer 

University in Osijek 

Slavica Singer 

Natasa Sarlija 

Sanja Pfeifer 

Djula Borozan 

Suncica Oberman 

Peterka 

Ministry of Economy, Labour and 


SME Policy Centre – CEPOR, 

Zagreb 

J.J. Strossmayer University in Osijek – 

Faculty of Economics, Osijek 

Puls, d.o.o., 

Zagreb 

104


Members 


APS Vendor 

Denmark 

University of 

Southern Denmark 


Torben Bager 

Hannes Ottossen 

Kim Klyver 

Kent Wickstrøm 

Jensen 

Majbritt Rostgaard Evald 

Suna L. Nielsen 

International Danish 


Academy (IDEA) 

Institute for 

Business 

Cycle Analysis 

Dominican 

Republic 

Pontificia 

Universidad Católica 

Madre y Maestra (PUCMM) 

Guillermo van der Linde 

Cecilia Pérez 

Maribel Justo 

Alina Bello 

José Rafael Pérez 

Tania Canaán 

Grupo Vicini 

International Financial Centre 

of the Americas 

Consejo Nacional de 

Competitividad 

Gallup República 

Dominicana 

Ecuador 

Escuela Superior Politécnica 

del Litoral (ESPOL)-ESPAE 

Graduate School of 

Management 

Virginia Lasio 

Ma. Elizabeth Arteaga 

Guido Caicedo 

Edgar Izquierdo 

Escuela Superior Politécnica del Litoral 

(ESPOL) 

Survey Data 

Egypt 

The British University in 

Egypt (BUE) 

David Kirby 

Nagwa Ibrahim 

Hala Hattab 

Industrial Modernization Center, Ministry of 

Trade & Industry 

ACNielsen 

Egyptian Junior Business 

Association (EJB) 

Amr Gohar 

Ahmed Nafie 

ACNielsen 

Finland Turku School of Economics Anne Kovalainen 

Tommi Pukkinen 

Jarna Heinonen 

Pekka Stenholm 

Pia Arenius 

Erkko Autio 

Ministry of Employment and the Economy 

Ministry of Education 

The European Union under the European 

Regional Development Fund and the 

European Social Fund 

Turku School of Economics 

Taloustutkimus Oy 

France EMLYON Business School Olivier Torres 

Danielle Rousson 

Caisse des Depots 

CSA 

Germany 

University of Hannover 

and 

Institute of Labour Market 

Research, Nuremberg 

Rolf Sternberg 

Udo Brixy 

Christian Hundt 

Heiko Stüber 

Institute of Labour Market Research, 

Nuremberg 

INFAS 

Greece 

Foundation for Economic 

and 

Industrial Research (IOBE) 

Stavros Ioannides 

Takis Politis 

Aggelos Tsakanikas 

Evaggelia Valavanioti 

Hellenic Bank Association 

Datapower SA 

Hungary 

University of Pécs, 

Faculty of Business and 

Economics 

László Szerb 

Zoltan J. Acs 

Attila Varga 

József Ulbert 

Siri Terjesen 

Péter Szirmai 

Gábor Kerékgyártó 

Ministry for National Development and 

Economy 

University of Pécs, Faculty of 

Business and Economics 

Ohio University (USA) 

Szocio-Gráf Piac-és 

Közvéleménykutató 

Intézet 

105


Members 


APS Vendor 

Iceland 

RU Centre for Research on 

Innovation and 


(Reykjavik University) 

Rögnvaldur Sæmundsson 

Silja Björk Baldursdóttir 

Reykjavik University 

Prime Minister’s Office 

Capacent Gallup 

India 

Pearl School of Business, 

Gurgaon 

Ashutosh Bhupatkar 

I. M. Pandey 

Janakiraman Moorthy 

Gour C. Saha 

Pearl School of Business, Gurgaon 

Metric Consultancy 

Iran University of Tehran M .Ahamadpour Daryani 

Abbas Bazargan 

Nezameddin Faghih 

Caro Lucas 

A. A. Moosavi-Movahedi 

A. Kord Naeij 

S.Mostafa Razavi 

Leyla Sarafraz 

Jahangir Yadollahi Farsi 

Mohammad Reza Zali 

Ministry of Labour and Social Affairs 

Dr. Mohammad 

Reza Zali 

Ireland Dublin City University Paula Fitzsimons 

Colm O’Gorman 

Enterprise Ireland 

Forfás 

Allied Irish Bank 

IFF 

Israel 

The Ira Center of Business, 

Technology & Society, Ben 

Gurion University of the 

Negev 

Ehud Menipaz 

Yoash Avrahami 

Miri Lerner 

The Ira Center of Business, 

Technology & Society, 

Ben Gurion University of the Negev 

The Brandman 

Institute 

Italy Bocconi University Guido Corbetta 

Alexandra Dawson 

Ernst & Young 

Atradius Credit Insurance 

Target Research 

Jamaica 

University of Technology, 

Jamaica 

Vanetta Skeete 

Claudette Williams-Myers 

Garth Kiddoe 

Girjanauth Boodraj 

Joan Lawla 

Louise Marcelle-Peart 

Faculty of Business and Management, 

University of Technology, Jamaica 

Koci Market 

Research and Data 

Mining Services 

Japan 

Keio University 

Musashi University 

Shobi University 

Takehiko Isobe 

Noriyuki Takahashi 

Tsuneo Yahagi 

Venture Enterprise Center 

Ministry of Economy, Trade and Industry 

Social Survey 

Research 

Information 

Co.,Ltd (SSRI) 

Latvia 

The TeliaSonera Institute at 

the Stockholm School of 

Economics in Riga 

Olga Rastrigina 

Vyacheslav Dombrovsky 

TeliaSonera AB 

SKDS 

Macedonia 

University “Ss. Cyril and 

Methodius” – Business 

Start-Up Centre 

Macedonian Enterprise 

Development Foundation 

(MEDF) 

Radmil Polenakovik 

Aleksandar Kurciev 

Bojan Jovanoski 

Tetjana Lazarevska 

Gligor Mihailovski 

Lazar Nedanoski 

Mexico Tecnológico de Monterrey Alejandro González 

Berenice Ramírez 

César Godínez 

Macedonian Enterprise Development 

Foundation (MEDF) 

Austrian Development Agency 

Macedonian Agency for Promotion of 


Tecnológico de Monterrey 

GfK Skopje 

Alduncin Y 

Asosiados, SA De 

CV 

106


Members 


APS Vendor 

Netherlands 

EIM Business and Policy 

Research 

Jolanda Hessels 

Sander Wennekers 

Chantal Hartog 

André van Stel 

Niels Bosma 

Roy Thurik 

Ingrid Verheul 

Dutch Ministry of Economic 

Affairs 

Stratus 

marktonderzoek bv 

Norway 

Bodo Graduate School of 


Lars Kolvereid 

Erlend Bullvaag 

Bjorn Willy Aamo 

Erik Pedersen 

Ministry of Trade and Industry 

Innovation Norway 

The Knowledge Fund, at Bodo 

Knowledge Park ltd. 

TNS Gallup 

Peru 

Centro de Desarrollo 

Emprendedor, 

Universidad ESAN 

Jaime Serida Nishimura 

Liliana Uehara-Uehara 

Jessica Alzamora Ruiz 

Universidad ESAN 

Imasen 

Romania 

Faculty of Economics and 


Administration, 

Babes-Bolyai University 

Ştefan Pete 

Lehel-Zoltán Györfy 

Ágnes Nagy 

Dumitru Matiş 

László Szerb 

Liviu Ilieş 

Comşa Mircea 

Annamária Benyovszki 

Tünde Petra Petru 

Ana Eugenia Matiş 

Mustatã Rãzvan 

Nagy Zsuzsánna-Ágnes 

Pro Oeconomica Association 

Babes-Bolyai University, Faculty of 

Economics and 

Business Administration 

Metro Media 

Transilvania 

Russia 

Saint Petersburg Team 


Management, Saint 

Petersburg 

Moscow Team 

State University - Higher 

School of Economics, 

Moscow 

Olga Verhovskaya 

Valery Katkalo 

Maria Dorokhina 

Alexander Chepurenko 

Olga Obraztsova 

Tatiana Alimova 

Maria Gabelko 

Graduate School of Management 

at Saint Petersburg State 

University 

State University - Higher School 

of Economics 

O+K Marketing & 

Consulting 

Levada-Center 

Serbia 

The Faculty of Economics 

Subotica 

Dusan Bobera 

Bozidar Lekovic 

Stevan Vasiljev 

Pere Tumbas 

Sasa Bosnjak 

Slobodan Maric 

Executive Council of Vojvodina Province, 

Department for Economy 

Marketing Agency 

“Drdrazen” d.o.o. 

Subotica 

Slovenia 

Institute for 

Entrepreneurship and 

Small Business 

Management, 

Faculty of Economics & 

Business, 

University of Maribor 

Miroslav Rebernik 

Polona Tominc 

Ksenja Pušnik 

Ministry of the Economy 

Slovenian Research Agency 

Smart Com 

Finance – Slovenian Business 

Daily 

RM PLUS 

South Africa University of Cape Town - 



Mike Herrington 

Jacqui Kew 

Penny Kew 

Tonia Overmeyer 

Department of Trade and Industry 

Swiss South Africa Cooperation Initiative 

South African Breweries 

Standard Bank 

SEDA 

Nielsen South 

Africa 

107


Members 


APS Vendor 

South Korea Jinju National University Sung-Sik Bahn 

Yong-Sam Lee 

Sanggu Seo 

Hyunsuk Lee 

Donna Kelley 

Small and Medium Business Administration 

(SMBA) 

Hankook Research 

Co. 

Spain 

Instituto de Empresa 

Ignacio de la Vega 

Alicia Coduras 

Isabel Gonzalez 

Cristina Cruz 

Rachida Justo 

DGPYMES 

Fundación Cultural Banesto 

Fundación Incyde 

IE Business School 

Instituto 

Opinòmetre 

S.L. 

Regional Teams: 

Andalucía 

Asturias 

Aragón 

Canary I. 

Cantabria 

Castille Leon 

Castille la 

Mancha 

Catalonia 

C. Valenciana 

Extremadura 

Galicia 

Madrid 

Murcia 

Navarra 

Basque Country 

Ceuta 

Melilla 

Regional Universities: 

Cádiz 

Oviedo 

Univ. de Zaragoza 

Las Palmas & La Laguna 

Univ. De Cantabria 

León 

Castille la Mancha 

Autónoma de Barcelona 

Miguel Hernández 

Fundación Xavier de Salas 

Santiago de Compostela 

Autónoma de Madrid 

Univ. de Murcia 

Pública de Navarra 

Deusto & Basque Country 

Univ. de Granada & Escuela 

de Negocios de Andalucía 

Regional Team Directors: 

José Ruiz Navarro 

Juan Ventura Victoria 

Lucio Fuentelsaz 

Rosa M. Batista Canino 

Fco. Javier Martínez 

Mariano Nieto Antolín 

Miguel Ángel Galindo Martín 

Carlos Guallarte 

José Mª Gómez Gras 

Ricardo Hernández 

Mogollón 

J. Alberto Díez de Castro 

Eduardo Bueno Campos 

Antonio Aragón Sánchez 

Iñaki Mas Erice 

Iñaki Peña Legazkue 

Lázaro Rodríguez Ariza 

María del Mar Fuentes 

Junta de Andalucía 

Gob. de Aragón 

Gob. del Principado de Asturias 

Gob. de Canarias, Cabildo 

Fondo Social Europeo 

Gob. de Cantabria 

Centros de Innovación 

Europeos (Navarra, Murcia, C 

y León) 

Generalitat de Catalunya 

Junta de Extremadura 

Air Nostrum, CEG, BIC Galicia 

IMADE, FGUAM 

Fundación Caja Murcia 

Eusko Ikaskuntza 

Instituto Vasco de Competitividad 

FESNA 

Universidad de Granada and many others 

Turkey Yeditepe University Nilüfer Egrican 

Esra Karadeniz 

Endeavor, Turkey Country Office 

Akbank 

Akademetre 

Research 

& Strategic 

Planning 

United Kingdom 

Hunter Center for 

Entrepreneurship, University 

of Strathclyde 

Economics & Strategy 

Group, Aston Business 

School, Aston University 

Jonathan Levie 

Mark Hart 

BERR Enterprise Directorate 

InvestNI 

Department of Enterprise, Trade 

and Investment (NI) 

Belfast City Council 

Enterprise Northern Ireland 

Hunter Centre for 

Entrepreneurship, University of 

Strathclyde 

Scottish Enterprise 

Welsh Assembly Government 

One North East 

North West Development Agency 

Yorkshire Forward 

Advantage West Midlands 

East Midlands Development 

Agency 

South West of England Development 

Agency 

South East Development 

Agency 

Enterprise Insight 

Wessex Enterprise 

IFF 

108


Members 


APS Vendor 

United States 

Babson College 

Baruch College, City 

University of New York 

SMU - Cox School of 


I.Elaine Allen 

William D. Bygrave 

Marcia Cole 

Monica Dean 

Ivory Phinisee 

Joseph Onochie 

Edward Rogoff 

Maria Minniti 


Baruch College 

Opinion Search 

Uruguay 

Instituto de Estudios 

Empresariales de 

Montevideo (IEEM) 

Leonardo Veiga 

Pablo Regent 

Fernando Borraz 

Alejandro Gaidana 

Adrián Edelman 

Cecilia Gomeza 

IEEM Business School - 

Universidad de Montevideo 

Mori, Uruguay 

GEM Global 

Coordination 

Team 

London Business School 

SMU - Cox School of 



Utrecht University 

IE Business School 

Michael Hay 

Mark Quill 

Chris Aylett 

Jackline Odoch 

Mick Hancock 

Maria Minniti 

William D. Bygrave 

Marcia Cole 

Jeff Seaman 

Niels Bosma 

Alicia Coduras 

Universidad del Desarrollo 


N/A 

109

110

111

112


University of Southern Denmark 

Focusing on learning for entrepreneurship, this study accounts for the changing entrepreneurship in 

Denmark. – Is the level of entrepreneurial activity in Denmark increasing, stable or declining, and is the 

Danish level higher or lower than in other societies? Are the cultural and institutional framework conditions 

in Denmark improving, stable or deteriorating, and are Danish conditions better or worse than in 

other societies? How do the framework conditions promote entrepreneurship, and what are the effects 

of culture and institutions? How are people in Denmark trained for entrepreneurship, during schooling 

and afterwards in various organizational contexts, and is training in Denmark expanding like in other 

societies? How are people’s training for entrepreneurship dependent on gender, age and education, and 

how does their training promote competency and networking? How are people’s vocation – entrepreneur 

or not – shaped by their learning? How are entrepreneurs’ education and training affecting their 

competency and networking to access resources such as advice? How are entrepreneurs’ innovativeness, 

exports and growth-expectations promoted by their learning. – These questions are addressed by 

analyzing data from our surveys in 2008 and preceding years in Denmark and many other countries, 

gathered mainly in our research program Global Entrepreneurship Monitor, GEM. The up-to-date surveys 

and analyses provide leading indicators of current changes in entrepreneurship. 

Thomas Schøtt studied at the University of Aarhus (cand.scient.), Columbia University (M.A. in statistics, 

and Ph.D. in sociology) and Yale University (postdoc in organizational research), was Assistant 

Professor and Associate Professor at the University of Pittsburgh, and is currently Associate Professor 

in the Department of Entrepreneurship and Relationship Management at the University of Southern 

Denmark. He teaches entrepreneurship, organizations, methodology and networks among people and 

organizations. He consults on entrepreneurship, intervention, organizations, clusters and development 

in local regions and in world regions. He researches entrepreneurship, innovation, knowledge, organizations 

and networks, and he has published several books and numerous articles in international journals 

and local media. As the National Team Leader of the Danish GEM team, he directs the Danish research 

program affiliated with the Global Entrepreneurship Monitor. He is currently writing a study of entrepreneurship 

as a global endeavour with variations among societies. More information can be gleaned from 

his homepage www.sam.sdu.dk/ansat/tsc 

ISBN 87-91070-32-7

Education, Training and Networking for Entrepreneurship in ...

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