GLOBAL ENTREPRENEURSHIP MONITOR - UDDnegocios.udd.cl/gemchile/files/2014/11/GEM-Expectativas... · GLOBAL ENTREPRENEURSHIP MONITOR 2005 Report on High-Expectation Entrepreneurship

Erkko Autio (HEC Lausanne)

GLOBAL ENTREPRENEURSHIP MONITOR

2005 Report on High-Expectation Entrepreneurship

GEM High-Expectation Entrepreneurship 2005

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Table of Contents

List of Tables..................................................................................................................................................................................2

List of Figures ................................................................................................................................................................................3

GEM Teams and National Sponsors ............................................................................................................................................4

Preface............................................................................................................................................................................................9

Executive Summary......................................................................................................................................................................10

Key Findings ............................................................................................................................................................................10

Introduction ................................................................................................................................................................................11

The GEM Method ........................................................................................................................................................................12

Sources of Data ......................................................................................................................................................................12

Definitions and Measurement of Nascent Entrepreneurship and Baby Businesses ..........................................................13

Definition of High-Expectation Entrepreneurial Activity ......................................................................................................13

The Importance of High-Expectation Entrepreneurship: Why it matters ................................................................................15

Prevalence of High-Expectation Entrepreneurial Activity in Different World Regions and Countries ....................................17

Conclusions ............................................................................................................................................................................21

Job Generation Potential of High-Expectation Firms ................................................................................................................22

Job Generation Potential by Country and World Region ......................................................................................................23

Conclusions ............................................................................................................................................................................29

Who is Behind High-Expectation Entrepreneurial Activity? ......................................................................................................30

Global Differences Between High-Expectation and Low-Expectation Nascent and Baby Businesses................................30

Differences between High-Expectation and Low-Expectation Nascent and Baby Businesses – Europe............................36

Prevalence Rates among Well Educated, High-Income Males ............................................................................................38

Conclusions ............................................................................................................................................................................40

National Entrepreneurial Framework Conditions and High-Expectation Entrepreneurship....................................................41

Conclusions ............................................................................................................................................................................45

Encouraging High-Expectation Entrepreneurship – Implications for Policy ............................................................................46

General Policy Implications ....................................................................................................................................................46

Conclusions for High-Income Countries ..............................................................................................................................47

Conclusions for Low-Income Countries ................................................................................................................................48

Endnotes ......................................................................................................................................................................................49

References ....................................................................................................................................................................................50

About the Sponsors ....................................................................................................................................................................52

This report and other GEM reports can be downloaded at: www.gemconsortium.org


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List of Tables

Table 1 GEM Participating Countries ..................................................................................................................................11

Table 2 GEM Method: Sources of Data................................................................................................................................12

Table 3 Job Creation Aspirations of Nascent entrepreneurs by Size Category: Combined GEM 2000 – 2004

Sample (44 Countries) ............................................................................................................................................22

Table 4 Job Creation Aspirations of Baby Businesses by Size Category ............................................................................23

Table 5 Job Creation Aspirations of Nascent Entrepreneurs and Baby Businesses by Size Category and

world Region: Asia and Africa ..................................................................................................................................24


world Region: North America, Latin America, and Oceania ..................................................................................25


world Region: Europe, EU Large Countries, EU Small Countries, and EU New Member Countries ..................27

Table 8 Job Creation Aspirations of Nascent Entrepreneurs and Baby Businesses by Size Category and Country:

Germany, Spain, Sweden, United Kingdom, and USA ..........................................................................................28

Table 9 Differences between High-Expectation and Low-Expectation Nascent Entrepreneurs – Europe ........................36

Table 10 Differences between High-Expectation and Low-Expectation Baby Businesses – Europe ..................................37

Table 11 Correlations between National Entrepreneurial Framework Conditions and the Population Prevalence of

High-Expectation Entrepreneurial Activity ..............................................................................................................43

Table 12 Correlations between National Entrepreneurial Framework Conditions and the Relative Prevalence of

High-Expectation Entrepreneurial Activity ..............................................................................................................44


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List of Figures

Figure 1 Total Entrepreneurial Activity by Growth Expectation in the GEM 2000 – 2004 Combined Dataset ..................17

Figure 2 Total Entrepreneurial Activity by Growth Expectation in Different world Regions: High-Expectation Nascent and Baby Businesses which Expect 20 or More Jobs in Five Years ........................................................18

Figure 3 Total Entrepreneurial Activity by Growth Expectation in Different European Areas: High-Expectation Nascent and Baby Businesses which Expect 20 or More Jobs in Five Years ........................................................19

Figure 4 Total Entrepreneurial Activity by Growth Expectation in Five Countries: High-Expectation Nascent and Baby Businesses which Expect 20 or More Jobs in Five Years ..............................................................................19

Figure 5 Total Entrepreneurial Activity by Growth Expectation in Different world Regions: High-Expectation Nascent and Baby Businesses which Expect 50 or More Jobs in Five Years ........................................................20

Figure 6 Total Entrepreneurial Activity by Growth Expectation in Five Countries: High-Expectation Nascent and Baby Businesses which Expect 50 or More Jobs in Five Years ..............................................................................20

Figure 7 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Overall Sample – Demographic Characteristics ..................................................................................................................................30

Figure 8 Comparison of High-Expectation and Low-Expectation Baby Businesses: Overall Sample – DemographicCharacteristics ..........................................................................................................................................................30

Figure 9 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Overall Sample – Employment Status ..................................................................................................................................................31

Figure 10 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Overall Sample – Employment Status ..................................................................................................................................................31

Figure 11 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Global Sample – Income Level and Education....................................................................................................................................32

Figure 12 Comparison of High-Expectation and Low-Expectation Baby Businesses: Global Sample – Income Level and Education....................................................................................................................................32

Figure 13 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Global Sample –Entrepreneurial Activities ........................................................................................................................................33

Figure 14 Comparison of High-Expectation and Low-Expectation Baby Businesses: Global Sample – Entrepreneurial Activities ........................................................................................................................................33

Figure 15 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Global Sample – Industry Sectors........................................................................................................................................................34

Figure 16 Comparison of High-Expectation and Low-Expectation Baby Businesses: Global Sample – Industry Sectors ..34

Figure 17 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Global Sample –Entrepreneurial Motivation ......................................................................................................................................35

Figure 18 Comparison of High-Expectation and Low-Expectation Baby Businesses: Global Sample – Entrepreneurial Motivation ......................................................................................................................................35

Figure 19 Prevalence Rates of Male, Well Educated, High-Income Nascent Entrepreneurs in Different Age Categories – GEM Global Sample ..........................................................................................................................38

Figure 20 Prevalence Rates of Male, Well Educated, High-Income Baby Business Managers in Different Age Categories – GEM Global Sample ..........................................................................................................................38

Figure 21 Prevalence Rates of Male, Well Educated, High-Income Nascent Entrepreneurs in Different Age Categories – GEM Europe Sample ..........................................................................................................................39

Figure 22 Prevalence Rates of Male, Well Educated, High-Income Baby Business Managers in Different Age Categories – GEM Europe Sample ..........................................................................................................................39


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GEM Teams and National Sponsors

GEM Teams and Sponsors 2000 - 2004

Title Location Members

Interim Executive London Business School Mick HancockDirector

Research Babson College Maria MinnitiDirector

Operations London Business School Stephen Hunt Director

Coordination Babson College William D. BygraveTeam Stephen Spinelli

Marcia Cole

London Business School Michael HayTatiana Schofield

Team Institution Members Financial Sponsor APS Vendor

Argentina Center for Entrepreneurship, Silvia Torres Carbonell IAE Management and Business School MORI ArgentinaIAE Management and Hector Rocha HSBC Private Equity Latin AmericaBusiness School Florencia Paolini Banco GaliciaUniversidad Austral

Australia Australian Graduate School Kevin Hindle Westpac Banking Corporation Australian Centreof Entrepreneurship Allan O'Connor for Emerging

Technologies and Society

Belgium Vlerick Leuven Gent Mirjam Knockaert Vlerick Leuven Gent Management SNT BelgiumManagement School, Bart Clarysse SchoolUniversiteit Gent Frank Verzele Flemish Ministery of Economic Affairs

Sophie Manigart (Steunpunt Ondernemerschap, Hans Crijns Ondernemingen en Innovatie)Tom Vanacker Walloon Ministery of Economic AffairsTrees De Bock

Brazil GEMBRAS - Grupo de Estudos Simara Maria S. S. Greco SEBRAE- Serviço Brasileiro de Apoio às Instituto Bonilhae Estímulo ao Empreendedorismo Marcos Mueller Schlemm Micro e Pequenas Empresasno Brasil Paulo Alberto Bastos Junior Instituto Euvaldo Lodi no Parana

Rodrigo Rossi Horochovski IEL/PR Joana Paula Machado

Canada HEC-Montréal Nathaly Riverin HEC Montréal SOMUniversity of British Columbia Louis-Jacques Filion Chaire d'entrepreneuriat Rogers J.A. (UBC) Daniel Muzyka Bombardier

Ilan Vertinsky Développement économique CanadaVictor Cui pour les régions du QuébecQianqian Du W. Maurice Young EntrepreneurshipAviad Pe'er and Venture Capital Research Center

Venture Capital CenterThe Sauder School of Business

Chile Centro de Entrepreneurship Jose Ernesto Amoros Grupo Santander Chile AdimarkGrupo Santander German EchecoparUniversidad Adolfo Ibanez Marina Schorr

Centro para el Emprendimiento Patricio Cortes Universidad del Desarrolloy la Innovacion Tomas FloresUniversidad del Desarrollo

China National Entrepreneurship Jian Gao School of Economics and Management, AMICentre, Tsinghua University Tsinghua University Synovate

National Entrepreneurship researchCentre of Tsinghua University


Chinese Taipei National Taiwan University Chen-en ko Small and Medium Enterprise Graduate Institue Jennifer Hui-ju Chen Administration, Ministry of Economic of Applied Hsiu-te Sung Affairs StatisticsChien-chi Tseng

Croatia J.J. Strossmayer University in Slavica Singer Ministry of Economy, Labour and Puls, d.o.o., Osijek Sanja Pfeifer Entrepreneurship Zagreb

Djula Borozan SME Policy Centre - CEPOR, ZagrebNatasa Sarlija Open Society Institute - Croatia, ZagrebSuncica Oberman Peterka J.J. Strossmayer University in Osijek

- Faculty of Economics, Osijek

Denmark Centre for Small Business Studies, Thomas Schøtt Erhvervs- og Byggestyrelsen IFKAUniversity of Southern Denmark Torben Bager IRF - Industriens Realkredifond

Lone Toftild Syddansk UniversitetKim Klyver Danfoss - Mads Clausens fond

VaekstfondenErnst & Young (Denmark)Boersen

Ecuador Escuela superior Virginia Lasio Escuela superior Politecnica del Litoral MARKETPolitecnica del Litoral Alicia Guerrero Montegegro (ESPOL University) ASOMARKET Escuela de Postgrado en Edgar Izquierdo Petroleos del Pacifico Cia. Ltds.administración de Empresas Guido Caicedo (PACIFPETROL S.A.)(ESPAE) Karen Delgado Arevalo Camara de comercio de Guayaquil

Elizabeth ArteagaVictor Osorio Cevallos

Finland Helsinki University of Technology Markku Maula Ministry of Trade and Industry Statistics FinlandErkko Autio Tekes

Turku School of Economics and Anne KovalainenBusiness Administration Pia Arenius

Tommi PukkinenJarna Heinonen

France EM Lyon Oliver Torres Caisse des Depots et Consignations CSAAurélien Eminet Observatoire des PMEDanielle Rousson

Germany Department of Economic and Rolf Sternberg Kreditanstalt für Wiederaufbau (KfW) Taylor NelsonSocial Geography, Ingo Lückgen Institut für Arbeitsmarkt - und Sofres EMNIDUniversity of Cologne Heiko Bergmann Berufsforschung (IAB)

Greece Foundation for Economic and Stavros Ioannides Greek Ministry of Development Metron AnalysisIndustrial Research (IOBE) Takis Politis IOBE Sponsors Datapower SA

Aggelos Tsakanikas Bank of AtticaChipita SA

Hong Kong The Chinese University Bee-Leng Chua Trade and Industry Department, SME Consumer Searchof Hong Kong David Ahlstrom development Fund, Hong Kong

Kevin Au Gov-ernment SARChee-Keong LowShige Makino Asia Pacific Institute of Business, Hugh Thomas The Chinese University of Hong KongSiu-Tong Kwok

Shenzhen Academy of Social Le Zheng Chinese Executive Club, Hong KongSciences Wang Weili Management Association

Dong Ziaoyuan

Hungary University of Pécs László Szerb Ministry of Economy and Transport Szocio-GráfUniversity of Baltimore (USA) Judit Károly Piac-és

Zoltán Acs Közvélemény-kutató Intézet

Iceland Reykjavik University Rognvaldur Sæmundsson Reykjavik University Gallup - IcelandElin Dora Halldórsdóttir The Confederation of Icelandic Employers

New Business Venture Fund Prime Minister’s Office


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India Indian Institute of Management Mathew Manimala N.S. Raghavan centre for Entrepreneurial Scopelearning, IIM Bangalor AC Nielsen

Ireland University College, Dublin Paula Fitzsimons Enterprise Ireland InterTradeIreland Lansdowne Colm O'Gorman Market Research Frank Roche Ltd.

iff

Israel Tel Aviv University Miri Lerner Israel Small Business Authority The B.I. CohenThe Academic College of Dalia Zelikovitz The Evens Foundation Institute for PublicTel-Aviv-Jaffa Amram Turjman Opinion Research

at Tel Aviv University

Italy Università Commerciale Guido Corbetta Bocconi University NomesisLuigi Bocconi Alexandra Dawson

Ugo Lassini

Japan Research Institute for Economics Takehiko Isobe Venture Enterprise Center SSRI& Business Administration,Kobe University

Musashi UniversityKeio University Noriyuki Takahashi

Tsuneo Yahagi

Jordan Young Entrepreneurs Association Wafa Nimri Ministry of Planning and International Al Jidara Khaled al Kurdi Cooperation Pro Group Usama Jacir Con-sultingGabi Afram

Ministry of Planning and Naseem RahahlehInternational Cooperation

Korea Soongsil University Jun Duck Yoon BK21 Ensb Program Hankook Yun Jae Park ResearchYoung Soo Kim

Mexico EGADE Marcia Campos Tec de Monterrey Alduncin y EGAP-Strategic Studies Centre Jaime Alonso Gomez Asociados

Elvira Naranjo

Netherlands EIM Business and Policy Research Jolanda Hessels Dutch Ministry of Economic Affairs Survey@Andre van StelSander WennekersNiels BosmaRoy Thurik Lorraine UhlanerIngrid Verheul

New Zealand New Zealand Centre for Howard Frederick Te Puni Kokiri DigipollInnovation and Entrepreneurship, Graeden Chittock (Ministry of Maori Development)UNITEC New Zealand Dean Prebble

Alex MaritzFranceen ReihanaMihipeka SisleyHelmut Modlik

Norway Bodø Graduate School of Business Lars Kolvereid Inovation Norway TNSMinistry of Trade and Industry Bodø Graduate School of Business Kunnskapsparken Bodø AS,Center for Innovation and Entrepreneurship

Peru Centro de Desarrollo Emprendedor, Jaime Serida Escuela de Administracion de Negocios SAMIMP – Escuela de Administracion de Armando Borda para Graduados (ESAN) Research Negocios para Graduados (ESAN) Oswaldo Morales Deltron Computer Wholesalers S.A. International

Meter Yamakawa


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Poland The Bachalski Educational Austin Campbell Polish Agency for Enterprise Development AC NielsenFoundation Roma Szapka The Karol Adamiecki University of

Krzysztof Bacawski Economics in KatowiceMaciej Koczerga The Poznan University of EconomicsPrzemysaw Zbierowsk AC Nielsen Poland

National Bank of Poland

Portugal Faculdade de Economia da Sara Medina POEFDS – Progama operacional do MetrisGfKUniversidade Nova de Lisboa Augusto Medina Emprego, Formaao e DesenvolvimentoSociedade Portuguesa de Inovaao Douglas Thompson Social

Manuel BaganhaRita CunhaStuart Domingos

Singapore National University of Singapore Poh Kam Wong Economic Development Board of Joshua ResearchLena Lee Singapore ConsultantsFinna Wong National University of Singapore

Slovenia Institute for Entrepreneurship, Miroslav Rebernik Ministry of Education, Science and Sports GralIteoUniversity of Maribor Polona Tominc Ministry of the Economy

Ksenja Pusnik SmartComChamber of CraftFinance - Slovenian Business Daily

South Africa UCT Centre for Innovation and Mike Herrington Liberty Life AC Nielsen ZAEntrepreneurship, Eric Wood South African BreweriesThe Graduate School of Business, John Orford The Shuttleworth FoundationUniversity of Cape Town Marlese von Broembsen

Spain Instituto de Empressa Alicia Coduras Instituto de Empresa OpinòmetreRachida JustoIgnacio de la VegaCristina CruzMaria Pia Nogueria

Sweden ESBRI Entrepreneurship and Magnus Aronsson Confederation of Swedish Enterprise SKOPSmall Business Research Institute Ministry of Industry, Employment and

CommunicationsSwedish Business Development Agency(NUTEK)Swedish Institute for Growth Policy Studies(ITPS)

Switzerland University of St. Gallen Thierry Volery KTI / CTI Taylor NelsonGeorges Haour KMU – HSG SofresHeiko Bergmann IMDBenoit Leleux

Thailand College of Management, Thanaphol Virasa College of Management, Mahidol AC NielsenMahidol University Brian Hunt University

Uganda Makerere University Thomas Walter European Union MUBSBusiness School Arthur Sserwanga Bank of uganda

Peter Rosa Makerere university business SchoolWaswa BarabasStefanie BarabasRebecca Namatovu

United Kingdom Co-ordination team Rebecca Harding Small Business Service iffJohanna Walker Barclays Bank PLC

East Midlands Development AgencyYorkshire ForwardMerseysideEnterprise InsightCountryside AgencyBritish Chamber of Commerce


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United Kingdom Scottish Team Catherine Currie Hunter Centre for Entrepreneurship iffHunter Centre for Entrepreneurship Jonathan LevieUniversity of Strathclyde

Welsh Team David Brooksbank Welsh Development Agency iffNational Entrepreneurship Dylan Jones-EvansObservatoryUniversity of GlamorganCardiff University

Northern Ireland Team Mark Hart Invest Northern Ireland iffSmall Business Research Centre, Maureen O'Reilly Belfast City CouncilKingston University Enterprise Northern IrelandEconomic Research Institute ofNorthern Ireland

United States Babson College Maria Minniti Babson College Opinion ResearchWilliam D. Bygrave Corp.Marcia ColeStephen Spinelli Andrew Zacharacis

Venezuela Instituto de Estudios Federico Fernandez Mercantil Servicios Financieros DataanalysisSuperiores de Administración Rebeca Vidal Fundacion IESA

Aramis Rodriguez


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Preface


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Over the past seven years, research by the GlobalEntrepreneurship Monitor (GEM) has demonstrated theimportance of entrepreneurship to the world economy,but this is the first time an academic study has focusedspecifically on high-expectation entrepreneurship on aglobal scale.

Having grown from small foundations into aninternational organisation itself, Mazars fosters a strongentrepreneurial spirit and recognises the impact of high-expectation entrepreneurs in job creation, innovation, andeconomic growth. As the findings in this report show,high-expectation entrepreneurs, though relatively few andfar between, are responsible for up to 80% of all jobscreated by entrepreneurs and their significance should notbe underestimated.

Mazars is proud to sponsor and support GEM in thisinitiative. Our hope is that this research not only providesvaluable insight into the phenomenon of high-expectationentrepreneurs, but also contributes to the wider policyimperative of governments and supra-nationalorganisations around the world for whom fosteringentrepreneurship and economic growth is of specificimportance.

David ChapmanPartner, International Chair of Advisory ServicesMazars

Executive Summary

Key Findings


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High-expectation entrepreneurial activity represents only asmall proportion of all entrepreneurial activity, yet itexplains the bulk of expected new jobs by cohorts ofnascent entrepreneurs and baby businesses. Dependingon country and world region, only some 3% to 17% ofnascent entrepreneurs and baby businesses expect toemploy 20 or more employees within five years. Onlysome 1 % to 7 % of nascent entrepreneurs and babybusinesses expect to employ 50 or more employees withinfive years. However, its economic potential is significant,as high-expectation entrepreneurs are responsible for upto 80% of total expected jobs by all entrepreneurs.

The rate of high-expectation entrepreneurial activity variessignificantly among world regions and individualcountries. The highest adult-age population-levelparticipation rate in high-expectation entrepreneurialactivity is observed for North America (Canada and USA),Anglo-Saxon countries1 (Australia, Ireland, New Zealand,United Kingdom, and USA), and Oceania (Australia andNew Zealand). For these regions, the population-levelprevalence rate of high-expectation entrepreneurial activityranges from approximately 1% to 1.6%. The lowest adultparticipation rate in high-expectation activity is observedfor European and highly developed Asian countries (HongKong, Korea, Japan, and Singapore), where this rate isapproximately 0.5%. In Spain, adult-age participation inhigh-expectation entrepreneurial activity (20+ expectedjobs) is only approximately 0.2%.

The prevalence rate of high-expectation entrepreneurialactivity in Europe and highly developed Asia is worryinglylow. There are no differences between European countrygroups (large EU countries, small European countries,new EU member countries), even though differences canbe observed between individual countries. Spain, inparticular, stands out because of its low participation ratein high-expectation entrepreneurial activity.

High household income, high education level, andopportunity motivation are most strongly associated withhigh-growth expectations. The greatest distinguishingelements for high-expectation entrepreneurial activity areobserved for income, education, and opportunitymotivation. These are suggestive of the individual-leveleconomic trade-offs related to the entrepreneurialdecision.

Population cells differ significantly in terms of high-expectation entrepreneurial activity. The highestprevalence observed for a single population cell (a‘population cell’ refers to a sub-group of the general

population, defined using one or several demographiccharacteristics such as age category, gender, andeducation level) is ten times higher than the population-wide participation rate in high-expectation entrepre-neurial activity. Of baby business managers, 25-34 yearold, high-income and well educated males displayed a4.4% participation rate in high-expectation activity –meaning that nearly one in twenty individuals in thispopulation cell are actively engaged with high-expectationentrepreneurial start-ups.

The relative prevalence of high-expectation activityappears positively associated with nationalentrepreneurial framework conditions. The correlationanalysis suggests that the anatomy of entrepreneurialactivity is more strongly associated with nationalconditions than is the overall prevalence of high-expectation activity. This may be associated withdifferential opportunities for entrepreneurship in high-and low-income economies.

Active policy has a role to play in promoting high-expectation entrepreneurial activity. Even though directcausal inferences are not possible from the presentanalysis, the evidence of differential relationships withnational conditions for different forms of entrepreneurialactivity suggests that there is room for activeentrepreneurship policy interventions.

Governments should be aware of the importance of high-expectation and high-potential entrepreneurial activity andconsider introducing highly selective support measuresand policies. To the extent to which the goal ofgovernment policy is to support job creation throughentrepreneurial activity, governments should be aware ofthe highly skewed distribution of job creation expectationswithin populations of nascent and baby businesses. Theyshould study who is behind high-potential entrepreneurialactivity, and how this kind of activity could be supported.Given the skewness of the distributions reported here,highly selective policy measures could prove moreeffective for job creation purposes than non-selectiveones.

1 We use the term ‘Anglo-Saxon’ to refer to English-speaking countries that have a strong ‘Anglo-Saxon’ tradition in terms of how their financialmarket institutions are structured.

Introduction


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According to empirical surveys, new firms create the bulkof new jobs. Studies also show that only a small sub-setof all new firms are responsible for the majority of all newjobs created through the entrepreneurial process. It isthus difficult to over-emphasize the importance of high-expectation entrepreneurial activity2 for economic andsocial wellbeing.

High-expectation entrepreneurial activity is also rare,which makes it very difficult to obtain first-hand empiricaldata on this important phenomenon. Thus far, there havebeen only a handful of studies of the economicimportance of high-growth new firms, and all of thesestudies have been limited to single countries. So far, therehave been no international surveys of high-expectationentrepreneurial activity. Thus, it is not known whether theprevalence of high-expectation entrepreneurial activityvaries between countries and world regions, and howhigh-expectation entrepreneurs differ from themainstream entrepreneurial phenomenon.

An international comparative analysis of high-expectationentrepreneurial activity is now possible because of theaccumulation of GEM adult population survey data. Thisreport presents the first global survey of the high-expectation entrepreneurial phenomenon. This is the firstreport to provide an overview of the prevalence of high-expectation entrepreneurial activity in different countriesand world regions. This is also the first empirical reporton the anatomy of the high-expectation entrepreneurialactivity in different countries and world regions, as well ason the characteristics of the individuals who report high-expectation entrepreneurial activity. Finally, this is the firstreport to analyze bivariate relationships between high-expectation prevalence rates and national entrepreneurialframework conditions, as captured by GEMs annualsurveys of national experts.

The Global Entrepreneurship Monitor (GEM) was createdin 1997 to study the economic impact of, anddeterminants of, national-level entrepreneurial activity.With its coverage of 44 countries worldwide, GEM is thelargest co-ordinated research effort ever undertaken tostudy population-level entrepreneurial activity. Because ofits worldwide reach and rigorous scientific method, GEMhas become the world’s most influential and authoritativesource of empirical data and expertise on theentrepreneurial potential of nations. GEM operates as anot-for-profit international academic research consortium3

in over 40 countries and produces both annual executivereports for public consumption, special topic reports on

selected issues, as well as academic research published inleading academic journals.

Co-founded by London Business School and BabsonCollege in 1997 with the contribution of leading scholars4,GEM has expanded from a comparison of 10 countries in1999 to 34 countries in 2004. Over the years, 44 countrieshave participated in GEMs annual adult populationsurveys. The participating countries of GEM in differentyears are listed in Table 1.

2 In this report, ‘high-expectation entrepreneurial activity’ refers to entrepreneurial firms (nascent and new) that expect to achieve rapid growth inemployment size.

3 This consortium operates under an umbrella organization called GERA, or the Global Entrepreneurship Research Association.4 Actively contributing founding team members of GEM were Professor Michael Hay (currently of London Business School), Professor Bill Bygrave

(Babson College), Professor Paul D. Reynolds (Florida International University), Dr Jonathan Levie (University of Strathclyde Hunter Center ofEntrepreneurial Management), Professor Erkko Autio (HEC Lausanne), and Professor Harry J. Sapienza (University of Minnesota).

Table 1 GEM Participating Countries

The GEM Method


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Sources of DataGEMs core activity is the annual compilation of primaryempirical data from its member countries on the adult-age population’s participation in entrepreneurial activity.On the basis of this data, GEM calculates the TotalEntrepreneurial Activity (TEA) rate for each participatingcountry. The TEA rate represents the share of working-and adult-age individuals (18 – 64 years old) who areeither actively trying to start new entrepreneurialcompanies, or who are currently acting as owner-managers of new entrepreneurial companies. In eachparticipating country, at least 2,000 randomly selectedadult-age individuals are interviewed by professionalsurvey interviewers, either by telephone or in person5. Inaddition to entrepreneurial activities, the interviews alsogather data on each person’s attitudes and beliefsregarding prevailing social and cultural norms, data onthe start-up attempt, as well as demographic data on therespondent6. This data enables detailed analysis of theanatomy of entrepreneurial activity in each GEM country;this is analyzed and reported in detail in GEMs countryreports.

The adult population survey data is complemented withtwo additional data collection efforts. First, in eachparticipating country, GEM surveys experts knowledgeable

of national conditions for entrepreneurial activity. Anextensive mail and interview survey questionnaire is usedand circulated among national policy-makers,entrepreneurs, financiers, consultants, representatives ofentrepreneurship support initiatives, entrepreneurshipacademics, and other individuals knowledgeable of thenational context for entrepreneurial activity. The datacollected by means of this survey is used for calculatingindices representative of national entrepreneurialframework conditions, or national conditions that havedirect bearing on entrepreneurial activity. Second is thecompilation of third-party data on the national economy,demographics, infrastructure, and other factors thatdescribe the country’s economic, structural, demographic,and social conditions for entrepreneurship. GEMs datasources are summarized in Table 2.

Typically, GEM annual and special reports are based on asingle year of data. Because the focus of the presentreport is on a small sub-set of the overall entrepreneurialphenomenon, a combined dataset covering years 2000 –2004 is used. Thus, the findings of the present report arebased on a rather sizeable dataset which contains over505,000 interviews of adult-age individuals in 44 countriesduring years 2000 to 2004.

5 Individual participating countries can collect significantly more data, up to 20,000 – 30,000 interviews.6 A detailed account of the GEM method is provided in: Reynolds, P., Bosma, N., Autio, E., Hunt, S., De Bono, N., Servais, I., Lopez-Garcia, P., &

Chin, N. (2005). Global Entrepreneurship Monitor: Data Collection Design and Implementation 1998 – 2003. Small Business Economics, 24: 205 – 231.

GEM Adult Population Survey

GEM National Expert Survey

GEM Secondary Data

A telephone and interview survey conducted by a polling organization in each GEMcountry of a minimum of 2,000 randomly selected respondents. The data isharmonized to be representative of the adult-age (18-64 years old) population of thecountry.

Combined mail questionnaire and interview survey of at least 36 national experts ineach GEM country knowledgeable of national framework conditions forentrepreneurial activity. The survey questionnaire collects data on finance, policy,government programs, education and training, technology transfer, physical andbusiness service infrastructure, market openness, social and cultural norms, IPRprotection, female entrepreneurship, as well as policies specifically dedicated at high-growth firms.

Compilation of data from third sources that describe general national conditions:national economy, demographics, society, infrastructure, and institutions. Data iscompiled from publicly available sources such as United Nations, World Bank, OECD,and dedicated international surveys.

Table 2 GEM Method: Sources of Data

Data Source Description


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Definitions and Measurement of NascentEntrepreneurship and Baby BusinessesIdentifying individuals active in starting a new business isnot a trivial task in survey interviews. To arrive at anaccurate definition which enables meaningful globalcomparisons over a wide range of countries, the GEMmethod employs a set of questions which has beenrefined over several years and tens of thousands of testinterviews. For its purposes, GEM distinguishes betweentwo types of entrepreneurial activity:

1 Nascent entrepreneurs are individuals who are activelytrying to start a new firm, but who have not done so asyet.

2 Baby business managers are owner-managers of a new,entrepreneurial firm which is younger than 42 monthsold7.

GEMs data on nascent entrepreneurship, or‘entrepreneurs in the making’, provides insight into alargely hidden aspect of grass-root level entrepreneurialactivity. New firms may have a long gestation periodbefore they are officially incorporated. Many new firmcreation processes are never taken to conclusion. Becausethe gestation period can be lengthy, it is important tostudy nascent entrepreneurial activity in order to gain abetter understanding of what factors drive new firmformation in different countries.

GEMs data on baby business managers provides anoverview of national-level entrepreneurial activity in newfirms already created. This aspect of the GEM adultpopulation survey thus provides a more traditionalperspective to entrepreneurship in new firms.

Several filtering questions are used to correctly identifyenterprising individuals. To be classified as a nascententrepreneur, an individual must meet the following fourcriteria:

1 The respondent has, during the past 12 months, donesomething tangible in order to advance his or her newfirm project (e.g., draft a business plan, study theopportunity, look for premises, etc).

2 The respondent will own all or part of the new firm tobe created.

3 The respondent will actively participate in the day-to-day management of the new firm.

4 The new firm in question has not yet paid salaries toanyone for more than three months.

The first criterion ensures that the project is a seriousone, and the individual is not simply toying with an ideaor a dream. The second criterion ensures that theindividual assumes personal financial risk in the project –an element not present in, for example, many corporateventures. The third criterion ensures that the person isnot merely an investor, but rather, a future entrepreneurin the making. The fourth criterion ensures that this is anew firm creation process and not an already establishedfirm: many individuals have a tendency to provide ratherlow-key assessments of their own firms, even though thefirm might already be an up-and-running operation.

To be classified as a baby business manager, therespondent needs to meet the following criteria:

1 The respondent is currently actively managing a newfirm.

2 The new firm in question has been established duringthe preceding three calendar years (i.e., it is less than42 months old).

3 The respondent owns all or part of the new firm.

The first criterion ensures that the respondent is notsimply an owner of the new firm. The second criterionsorts new entrepreneurial firms from old, establishedones. The third criterion ensures that the respondent hasa personal stake in the new firm and is not simply a hiredmanager.

Because of the use of several filtering questions, there isgood confidence that the GEM data provides a true andaccurate view of new firm creation and start-up processesin different countries.

Definition of High-ExpectationEntrepreneurial ActivityGEMs Total Entrepreneurial Activity (TEA) rate indicatesthe country-level prevalence of both nascententrepreneurs and baby business managers in theworking-age population. The TEA rate thus indicates theshare of all working-age individuals active in creating andrunning new firms, regardless of the ambition level of thenew venture. Because the bulk of new firm activity is notvery ambitious, the TEA rate effectively reflects the level oflow-ambition activity in GEM countries.

In this report, our interest is on a specific facet ofentrepreneurial activity – high-expectationentrepreneurship. This is possible using GEMs data ongrowth expectations. GEM analyzes growth expectations

7 This figure is due to the fact that the GEM adult population surveys are normally carried out in May-June. The survey defines baby businesses as allfirms that have been created during the preceding three calendar years. For example, the 2005 GEM survey asks if a given start-up attempt hasbeen created in year 2002 or later. As the question is asked in June, this gives a definition of baby business as younger than 3*12 months + 6months = 42 months.


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of nascent and baby businesses by asking how manyemployees the identified nascent entrepreneurs and babybusiness managers expect to employ in five years’ time.This estimate represents the new venture’s growthexpectations and is investigated separately for nascententrepreneurs and baby businesses.

There is no universally accepted definition of whatconstitutes a high-growth potential entrepreneurial firm.Some studies have based the definition of high growth onobserved distributions in populations of firms, defining,for example, the top 5-10% as ‘fast-growing’ (e.g., Birch,1987; Storey, 1996; Davidsson & Delmar, 2002; Almus,2002). Some studies have classified as ‘gazelles’ all firmsthat grow their organizational size (e.g., sales, number ofemployees) by more than 20% per annum during each ofthree to four consecutive years (Birch, 1997; Autio,Arenius, & Wallenius, 2000), often using some minimuminitial size criterion as a cut-off point. Some studies defineas fast-growth firms those who double their employmentsize within five years and create at least five jobs (Brüderl& Preisendörfer, 2000).

In this study, we define high-expectation activity as allnascent and baby businesses which expect to employ atleast 20 employees within five years’ time. This criterion isused because achieving the size of 20 employees is notsimple. At this size category, firms typically will havedeveloped internal specialization, there is an identifiablemanagement function, and there is usually at least someseparation of ownership and employees, in the sense thatnot all employees are also owners of the company.

In this report, we look at the prevalence of high-growthexpectations among both nascent entrepreneurs and babybusinesses, as identified in GEMs adult populationsurveys from the years 2000 to 2004. The followingdefinitions are therefore used throughout the report,unless otherwise stated:

- High-Expectation Nascent Entrepreneur is an individualwho expects to employ at least 20 employees withinfive years’ time through his or her new firm.

- High-Expectation Baby Business is a new firm, up to 42months old, that aims to employ at least 20 employeeswithin five years’ time.

In some of the analyses that follow, we will be using datarepresenting different levels of growth expectations suchas the goal of employing 2, 5, 10, and 50 employees infive years’ time.

In this report, the term “high-expectation” is used toemphasize the fact that the GEM operationalization isbased on expected, rather than realized, job creation.

While not all expectations are materialized, growthaspirations have been shown to be a good predictor ofeventual growth (Davidsson, 1989; Liao & Welsch, 2003).However, there may be situations where the growth of thefirm may come as a surprise to its owners. The GEM data,therefore, should be read as indicative of reasonableexpectations, maybe even of aspirations of individualfirms and individuals.


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Almost no one today would seriously contest theimportance of entrepreneurial activity in economic andsocietal life. New firms are seen as important generatorsof jobs (e.g., Birch, 1987, 1995; Fölster, 2000; Storey, 1994;Acs 1998). They also smooth the functioning of markets,potentially enhancing economic efficiency and productivity(Baumol, 1990, 2002). Numerous studies suggest that newfirms can have an important role to play in innovation inselected sectors (e.g., Acs, 1996; Audretsch, 2002;Michelacci, 2003). New firms may also operate as animportant alternative employment mechanism for manysubsets of the adult-age population.

From the policy perspective, in addition to theircontribution to competition, arguably the most importantaspect of new firms concerns their significantcontributions to job creation. Two highly significantfindings emerge from received empirical studies. First,new firms contribute to a major proportion of all new jobsin many national economies. Second, the greatestpotential for job creation appears to be concentrated into avery small sub-group of all new firms.

The job creation potential of new firms has receivedparticular attention among researchers. Perhaps the mostinfluential researcher to underscore the importance ofnew, entrepreneurial firms for job creation was David Birch(1979) who reported that new firms accounted for the bulkof new job creation in the USA, while large, establishedfirms were net destroyers of jobs. Even though Birch’searly findings overstated the importance of new firms injob creation, their job creation potential has subsequentlybeen confirmed by numerous other studies in several(mostly industrialized) countries (e.g., Kirchhoff, 1994;Storey, 1994; Davidsson, Lindmark & Olofsson, 1994;Westhead & Cowling, 1995; Davidsson & Delmar, 2003;Acs, 1998)8. According to these studies, and depending onthe phase of the economic cycle, new firms may beresponsible for anything from one-third up to the totalityof net job creation. While a part of job creation by newfirms undoubtedly reflects downscaling and restructuringof established firms, and therefore, job migration ratherthan job creation, economists are in agreement that thegenuine job creation potential of new firms is alsosignificant. Moreover, it should be remembered that newfirms are highly dynamic: they tend to depict high numbersboth in terms of job creation and in terms of jobdestruction, as the mortality rate of start-up companies ishigher than for older companies (e.g., Acs, 1998; Aghion &Howitt, 1992). Even controlling for the job destructioneffect, however, the net effect remains positive. In arigorously conducted study, Fölster (2000) found that

every self-employment decision meant the net creation of1.3 new jobs in Sweden, after the effects of variousintervening mechanisms were controlled. According to theLongitudinal Establishment and Enterprise Microdata(LEEM) database, new establishments created 69% of netnew jobs in the US from 1990 to 1995, and new firm start-ups which did not exist prior to 1990 created 22% of newjobs (Acs, 1998; Audretsch, 2002). Thus, the potency ofnew and small firms to create new jobs as such is not indispute, even though the contribution of new and smallfirms to job creation may vary over the economic cycle andin different economic contexts.

There is more, however, to new firms and job creationthan their aggregate contribution to job creation. From apolicy perspective, it is equally important to recognize thatthe anatomy of entrepreneurial firms’ job creationprocesses is far from smooth. Several studies suggest thatonly a relatively small proportion of all new firms end upgenerating the bulk of new jobs. In this regard, evidencevaries according to region. In the United Kingdom, Storey(1994) found that only 4% of new firms born in any givenyear accounted for 50% of all the jobs created by thesurviving firms within that cohort after 10 years hadelapsed. Kirchhoff (1994) found that the 10% of fastest-growing firms contributed to three-quarters of new jobsduring an eight-year observation period within a cohort offirms started in the US in 1978. According to Birch et al.(1997), the so-called gazelles accounted for more than70% of the employment growth in the U.S. between 1992and 1996, while representing only about 3% of the firmpopulation. Autio, Arenius, and Wallenius (2000) foundthat, within a cohort of Finnish high-growth single-establishment companies, some 1% of top growerscreated some 40% of the aggregate impact over four years,both in terms of sales and employment growth (Autio,Arenius, & Wallenius, 2000). These studies are allsuggestive of a very significant concentration of jobcreation potential within populations of new,entrepreneurial firms, with only a small percentage of allnew firms contributing a disproportionate share of all newjobs within the population. It is noteworthy, however, thatmost of the received studies reporting significantcontributions by a small subset of new firms are fromAnglo-Saxon countries. There is some evidence that thiseffect may vary according to context. Significantly,Davidsson and Henrekson (2002) reported that, whilehigh-growers created a disproportionate share in theirpanel of Swedish firms from 1987 to 1996, the totalcontribution of high-growers to job creation was not verysignificant when all alternative sources of job contribution

The Importance of High-Expectation Entrepreneurship: Why it matters

8 Early studies on job creation potential overstated the potency of new firms in job creation because of the methods used. Today, the consensusremains, though, that new firms and new units by established firms are an important source of new jobs. For criticism on the methods of earlystudies see Davis & Haltiwanger (1994).


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were considered. Even though Davidsson and Henreksonemphasized that their findings were subject to a number ofcaveats, their study nevertheless raises the prospect thatthe level of job creation concentration may vary accordingto national context.

Overall, therefore, the empirical evidence points to strongjob creation contributions by new firms, as well as to ahighly uneven distribution of this contribution. Where theempirical evidence of the overall job creation potential isquite broad, studies on how this potential is distributedwithin populations of entrepreneurial firms are quite rare.This is because of the rarity of the high-growthphenomenon, and because of the lack of datasets suitablefor the study of firm-level job creation contributions. Themajority of all new companies operate in small marketniches and seldom harbor ambitions to grow beyond thesize of one to two full-time employees. Entrepreneurshipresearchers are thus faced with a dilemma: the mostinteresting aspect of national-level entrepreneurial activityis also the most elusive one. Because only a small fractionof all new companies aim for rapid growth, and becauseentrepreneurial activity, in general, can be typicallyobserved only in a small subset of adult-age population,observing this phenomenon empirically can beprohibitively difficult. To date, most studies on thephenomenon are ex-post studies, which look atentrepreneurial activity only after the results of this activityare known: we already know whether a given venture grewor not. There is very little empirical research on ex-antegrowth aspirations: who aspires for and expects rapidgrowth, and what factors are associated with suchexpectations.

From a policy-maker’s perspective, the above dilemma isnot without consequence. To design effective measures tosupport job creation through the entrepreneurial process,empirical data is required, pertaining to the time bothbefore and after firm-level growth processes have occurred.The scarcity of received empirical findings, particularly ongrowth expectations, thus constitutes a serious constraintto the design of effective job creation policies. Data isrequired both on the overall level of job creationcontributions by different entrepreneurial firms, on howthese contributions are distributed within populations ofentrepreneurial firms, on individuals who start rapidly-growing firms, as well as on which policies are effective.Received datasets enabling the study of such questions are

rare and constrained to single country contexts. Thelimitation of individual datasets to individual countries hasprecluded any systematic examination of the potentialeffect of the country environment on the prevalence of highexpectations. To date, there have been no datasets toenable meaningful international comparisons of theprevalence of high-expectation entrepreneurial activity.Received studies, such as those reviewed above, suggestthat there may be country differences in terms of high-expectation entrepreneurial activity. Given the pertinenceof new high-growth firms to job creation and the highpriority of employment for government economic policies,more empirical research on the high-expectationentrepreneurial phenomenon is urgently required.

So far, even the GEM consortium has not been able toanalyze high-expectation entrepreneurial activity because ofthe scarcity of the phenomenon. In most GEM countries,the Total Entrepreneurial Activity (TEA) rate, whichrepresents the combined share of nascent entrepreneursand baby business managers of working-age population(18-64 years), varies from a low of approximately 2% to ahigh of 10-12%9. High-expectation entrepreneurial activityrepresents only a small sub-set of the total TEA rate.Because of this, very large datasets per country or regionare required in order to estimate the prevalence of high-expectation activity with a reasonable degree of accuracy.The standard data collection protocol of GEM requires atleast 2,000 interviews annually for each participatingcountry. This is a large enough sample if one is interestedin estimating the overall TEA rate10. To provideassessments of high-expectation entrepreneurial activity,much larger sample sizes are required – preferably at least10,000 – 20,000 interviews. The high cost of gatheringsuch large datasets prohibits the estimation of high-expectation activity on an annual basis, and high-growthentrepreneurial activity has not been included in annualGEM reports.

Thanks to the data collection cycle of the GEM consortium,which annually collects new empirical data, it is possible toaddress the high-expectation phenomenon by combiningseveral years of GEMs adult population survey data11.GEM has been collecting data from a growing number ofcountries since 1998. As the consortium is now in itsseventh data collection cycle, sufficient data has nowaccumulated to enable more fine-grained analyses ofcountry-level entrepreneurial processes.

9 In some developing countries, such as Uganda, the TEA rate is significantly higher – up to 30%.10 Because of the scarcity of high-expectation entrepreneurial activity the overall TEA rate effectively reflects the prevalence of low-expectation

entrepreneurial activity within a given country.11 An important methodological constraint needs to be recognized. The procedure of combining several years of data carries the important assumption

of temporal stability. We are assuming that the prevalence of high-expectation entrepreneurial activity does not fluctuate importantly from one yearto another. From previous GEM reports we can see that the country-level TEA index does not vary importantly from year to year, so this assumptionshould be reasonable. We are also assuming that the prevalence of high-growth expectations among nascent entrepreneurs and baby businesses isstable from one year to another. There is less received data available to check the validity of this assumption, and it may well be that growthexpectations of nascent entrepreneurs vary more because of, e.g., prevailing economic conditions than does the overall level of entrepreneurialactivity in a given country. Finally, it is assumed that the external conditions affecting the prevalence of high-expectation entrepreneurial activity donot change radically from one year to another, or, alternatively, that high-expectation entrepreneurial activity reacts to changes in external conditionswith an important time lag. Based on previous GEM studies, this seems to be the case for overall entrepreneurial activity.


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At the macro level, there are two fundamental questionsconcerning high-expectation entrepreneurial activity. Thefirst concerns the prevalence of high-expectationentrepreneurial activity within a given country’s orregion’s adult-age population. The second concerns therelative prevalence of high-expectation entrepreneurswithin the sub-population of entrepreneurial firms. Thecombined GEM dataset for years 2000 to 2004 enables usto address both questions. The data on the overallprevalence of high-expectation entrepreneurial activitymakes it possible to compare countries and world regionsin this regard, as well as to detect differences betweenregions and countries. The data on growth expectationsamong nascent entrepreneurs and baby businessesenables us to check whether the anatomy of theentrepreneurial phenomenon differs from one country andworld region to another.

In this chapter, our interest is on the first question: Doesthe rate of high-expectation entrepreneurial activity varyfrom one world region and country to another? Weanalyze the prevalence of nascent and baby businessactivity with different levels of growth expectation. We useGEMs data on growth expectations to divide the overalldataset into smaller subsets. The distribution of totalentrepreneurial activity according to growth expectation isshown in Figure 1. The following categories of growthexpectations are used: (1) up to 1 employee in five years;(2) 2 or more employees; (3) 5 or more employees; (4) 10or more employees; (5) 20 or more employees; and (6) 50or more employees. The vertical bars indicate the 95%confidence intervals of the means. If the vertical bars oftwo regions do not overlap, the difference between theregions can be considered as statistically significant.

Total Entrepreneurial Activity (TEA) represents the shareof all adults (18 – 64 year olds) who are either nascententrepreneurs or baby business owner-managers. In total,for the combined 2000 – 2004 GEM sample, theserepresented 8.3% of the adult-age population within thecountries that participated in GEM during those years.

As can be seen, the TEA index is dominated byentrepreneurial activity that does not have high-growthexpectations. Only some 5.4% of the adult-age populationin the GEM 2000 – 2004 countries were active in nascentand baby businesses which expected to employ two or

more employees in five years’ time. Only 2.7% of theadult-age population expected to have five or moreemployees. For the growth expectations of 10+, 20+, and50+ employees, the percentages drop to 1.6%, 0.8%, and0.4%, respectively. In other words, only less than oneperson out of one hundred is involved with a nascent orbaby business which expects to create 20 or more jobs infive years. And, only four out of one thousand expect tocreate 50 or more jobs. Expectations of rapid growth arethus a rare phenomenon when considered at the level ofthe adult-age population.

Prevalence of High-Expectation Entrepreneurial Activity in Different WorldRegions and Countries

Figure 1 Total Entrepreneurial Activity by Growth Expectation in the GEM 2000 – 2004 Combined Dataset

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Figure 2 shows the prevalence of high-expectationentrepreneurial activity for different world regions. Here,individual countries are grouped into world regions in aneffort to achieve a large enough sample size formeaningful comparisons. In Figure 2, the prevalence of allnascent and baby businesses which expected 20 or morejobs in five years is shown. We can see that theprevalence of high-expectation entrepreneurial activityvaries importantly from one world region to another. Inthis comparison, North America (USA and Canada)stands out as having the highest prevalence of high-growth potential entrepreneurial activity, withapproximately 1.5% participation of the adult-agepopulation in this kind of activity. The level ofparticipation is also high for Anglo-Saxon countries12

(Australia, Canada, Ireland, New Zealand, UnitedKingdom, and USA), with a 1.4% participation rate. Asregions, Oceania (Australia and New Zealand) and LatinAmerica (Argentina, Brazil, Chile, Ecuador, Mexico, Peru,and Venezuela) come next, with 1.1% and 1.0%participation rates, respectively.

How high are these rates? In 2004, the TEA index forJapan was 1.5%. Thus, in North America, the prevalencerate of high-expectation entrepreneurial activity is equal toJapan’s total entrepreneurial activity rate. This is indicativeof a significant difference between Japan and NorthAmerica, given that high-expectation activity representsonly a sub-set of the TEA rate. Relatively speaking, as

many individuals in North America try to start, or areinvolved with, high-growth businesses as there areindividuals involved with any kind of nascent or babybusiness in Japan.

Of the world regions, the lowest participation rates inhigh-expectation entrepreneurial activity were observed forEuropean countries and for highly developed Asiancountries (Hong Kong, Japan, Singapore, and SouthKorea). In these regions, approximately 0.5% of adult-agepopulation participated in high-expectationentrepreneurial activity. There is thus significant variancebetween world regions in terms of high-expectationentrepreneurial activity participation rates, as the level ofNorth America is three times as high as that of Europe orhighly developed Asia.

Because 265,000 interviews in the dataset are fromEurope, it is possible to divide Europe into subsets. Thefindings are shown in Figure 3. No major differences canbe observed between EU large countries, small Europeancountries, Nordic countries, Benelux countries (Belgiumand Netherlands) and EU’s new member countries. Theparticipation rate in high-expectation (20+ expected jobsin five years) is around 0.5% -- significantly smaller thanGEMs overall average.

Figure 2 Total Entrepreneurial Activity by Growth Expectation in Different World Regions: High-Expectation Nascent andBaby Businesses which Expect 20 or More Jobs in Five Years

12 We use the term ‘Anglo-Saxon’ to refer to English-speaking countries that have a strong ‘Anglo-Saxon’ tradition in terms of how their financialmarket institutions are structured.

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The size of individual country datasets enables us toreasonably estimate the high-expectation participationrate of five countries: Germany, Spain, Sweden, UnitedKingdom, and USA. The data for these countries is shownin Figure 4. As can be seen, USA has the highest level ofparticipation in high-expectation (20+ expected jobs)entrepreneurial activity, with a participation rate ofapproximately 1.6%. United Kingdom and Germany comesecond and third, with participation rates of approximately0.7%. This is about half of the USA level. At 0.5%,Sweden’s participation rate is statistically significantlylower than in the United Kingdom and in Germany, but

double that of Spain (0.2%) for which the lowestparticipation rate is observed.

The country data means that whereas in the USA, nearlytwo persons out of one hundred are involved with high-expectation entrepreneurial activity, the correspondingrate is only two per thousand. The nearly eight-folddifference in the prevalence rates between USA and Spainis indicative of significant variation in high-expectationactivity prevalence rates. Even within the EU, thedifference between Spain, on the one hand, and UnitedKingdom and Germany, on the other, is three-fold.

Figure 3 Total Entrepreneurial Activity by Growth Expectation in Different European Areas: High-Expectation Nascentand Baby Businesses which Expect 20 or More Jobs in Five Years

Figure 4 Total Entrepreneurial Activity by Growth Expectation in Five Countries: High-Expectation Nascent and BabyBusinesses which Expect 20 or More Jobs in Five Years

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The prevalence rates of higher-growth activity (thosenascent entrepreneurs and baby businesses who expect50 or more jobs in five years) follow similar patterns tothose described above. Figure 5 shows the prevalence of

50 or higher job expectations in different world regions.Figure 6 shows the prevalence of 50 or higher jobexpectations for selected countries.

Figure 5 Total Entrepreneurial Activity by Growth Expectation in Different World Regions: High-Expectation Nascent andBaby Businesses which Expect 50 or More Jobs in Five Years

Figure 6 Total Entrepreneurial Activity by Growth Expectation in Five Countries: High-Expectation Nascent and BabyBusinesses which Expect 50 or More Jobs in Five Years

Overall, the participation levels in “50+ activity” areapproximately half the level of “20+ activity”. For regions,North America (USA and Canada) depicts the highestparticipation rate, with a mean participation level of 0.6%among the adult-age population. Note that the 95%confidence interval stretches from 0.5% to 0.7%. Thegroup of Anglo-Saxon countries, developing Asiancountries, and Oceania countries depicts similar levels, at

approximately 0.5%. The differences between the first fourgroups are not statistically significant. African and LatinAmerican participation rates are significantly lower thanthat of North America and Anglo-Saxon countries, atapproximately 0.3%. Europe and highly developed Asiancountries depict the lowest levels of “50+ activity”, with aEuropean participation rate of approximately 0.15%.

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Of the five individual countries, USA has the highestparticipation rate in “50+ activity”, at approximately 0.6%.Germany, Sweden, and United Kingdom are at the samelevel, at approximately 0.2-0.25%. Spain again emerges asthe country with the smallest participation in high-growthentrepreneurial activity, with a “50+ activity” participationrate of approximately 0.05%. Thus, approximately onlyone out of two thousand Spaniards is currently engagedin either starting or running a new business which expectsto employ 50 or more employees within five years’ time.

ConclusionsHigh-expectation entrepreneurial activity is rare.Depending on world region and country, onlyapproximately 1.5% or less of the adult-age population(18-64 year olds) is involved with nascent or babybusinesses that expect to employ 20 or more employeesin five years’ time.

The rate of high-expectation entrepreneurial activity variessignificantly among world regions and individualcountries. The highest adult-age participation rate in high-expectation entrepreneurial activity is observed for NorthAmerica (Canada and USA), Anglo-Saxon countries(Australia, Ireland, New Zealand, United Kingdom, andUSA), and Oceania (Australia and New Zealand). Forthese regions, the rate of high-growth entrepreneurialactivity ranges from approximately 1% to 1.6%. Thelowest rate of high-expectation activity is observed forEuropean and highly developed Asian countries (HongKong, Korea, Japan, and Singapore), where this rate isapproximately 0.5%. In Spain, adult-age participation inhigh-expectation entrepreneurial activity (20+ expectedjobs) is only approximately 0.2%.

The prevalence rate of high-expectation entrepreneurialactivity in Europe and highly developed Asia is worryinglylow. There are no differences between European countrygroups (large EU countries, small European countries,new EU member countries), even though differences canbe observed between individual countries. Spain, inparticular, stands out because of its low participation ratein high-expectation entrepreneurial activity. The policy-makers of these regions would be well advised to examinethe reasons for this high-expectation deficit.


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Job Generation Potential of High-Expectation Firms

The expected job creation by nascent entrepreneurs forthe overall sample is shown in Table 3. The job creationdata are shown separately for different size categories.The figures represent estimates based on a total sampleof 505,000 interviews, of which 410,402 individualsbelonged to the adult-age population. The figures belowthus represent the job generation expectations of nascententrepreneurs within a population of 410,402 adult-ageindividuals. The data has been weighted according to UNestimates for adult-age populations in June 2005 for the44 countries that participated in GEM from 2000 to 2004.The dataset has also been adjusted according to countrysample sizes.

Several interesting patterns can be observed in Table 3.First, in total, there were 18,869 nascent entrepreneursthat reported an expected number of employees withinfive years. This represents 4.6 % of the sample andprovides an idea of the harmonized level of nascententrepreneurial activity within the 44 GEM countries from2000 to 200413.

Second, the sample is dominated by nascententrepreneurs that depict very low levels of growthaspiration. Roughly one-third of all nascent entrepreneursexpected to employ, at most, one employee within five

years, and only one-third expected to employ fiveemployees or more. Those expecting to employ zero toone employees numbered 5,122 nascent entrepreneurs,representing 29.9% of the total population of nascententrepreneurs. Only 7,061 nascent entrepreneurs (37.4%of total) expected to employ five or more employeeswithin five years. This pattern is consistent with receivedstatistics which show that the majority of all new firmsgrow only very modestly or not at all.

Of the total sample, only 20.8% expected to reach the sizeof 10 or more employees within five years, and only 9.6%expected to reach the size of 20 or more employees. Thus,less then 10% of all nascent entrepreneurial activity canbe characterized as high-expectation start-up activity evenusing quite modest criterion. It is important to observe,however, that the high-expectation nascent entrepreneursrepresented over 70% of the total expected job creationwithin five years. This pattern is consistent with receivedempirical studies on actual job creation and underlinesthe importance of high-growth potential entrepreneurialactivity for job creation. The distribution of job creationexpectations is quite biased, as those expecting to create50 or more jobs represented only 5.3% of the populationof nascent entrepreneurs and promised to create as muchas 65.5% of all new jobs.

13 Note that this figure is different from GEMs TEA (Total Entrepreneurial Activity) index, which includes both nascent and baby business activity.Also, this average is based on a dataset that combines data from 2000 to 2004 and can thus be best interpreted as an average for that period.

14 Based on data received from the Panel Study of Entrepreneurial Dynamics (PSED) – see http://projects.isr.umich.edu/psed.

Table 3 Job Creation Aspirations of Nascent Entrepreneurs by Size Category: Combined GEM 2000 – 2004 Sample (44Countries)

In total, the nascent entrepreneurs expected to create238,833 new jobs within five years. This represents 12.7new jobs per each of the 410,402 individuals interviewed.This estimate should be read with some caution, as itrepresents expectations, not actual job creation. Empiricalstudies suggest that approximately one-half of nascententrepreneurial activity never leads to the actual creationof a new firm14. Also, even when a new firm is started, therealized job creation may fall short of expectations,particularly among the more ambitious nascententrepreneurs. Even with these reservations, the statisticsreported in Table 3 underline the potential of nascent

entrepreneurial activity in general, and high-expectationactivity in particular, for job creation.

Another perspective on high-expectation activity can beobtained by looking at baby businesses, or entrepreneurialfirms that are less than 42 months old. Unlike nascententrepreneurs, which represent start-up attempts that arenot yet operating new businesses, baby businesses havefirst-hand experience of business operations. Thus, theirgrowth expectations are likely to be more realistic. Dataon the job creation potential of baby businesses isreported in Table 4.


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The patterns observed for nascent entrepreneurs and babybusinesses are consistent with one another and alsoconsistent with received empirical studies on actual jobcreation. Storey (1994) reported that 4% of a cohort of newfirms in the United Kingdom created 50% of all jobscreated by the cohort. Birch (1997) reported that morethan 70% of the employment growth in the US from 1992to 1996 came from only 3% of all firms. Thus, even thoughthe data reported here only concerns expectations, ratherthan actual new jobs, the distributions are very similar towhat is known about actual job creation performanceamong entrepreneurial firms.

Because the GEM data reports expectations rather thanactual job output, it is not possible to verify whether thosereporting high job creation expectations will eventuallydeliver, or whether eventual growth will be influenced moreby random events than ex-ante aspirations. However, thereare several studies to suggest a link between growth intentand growth performance. Organizational growth seldomoccurs accidentally in entrepreneurial firms, becauseachieving growth typically requires significant investments,in terms of both resources and management time. Such aninvestment is not easily made if the intent of theentrepreneurial firm is not to grow. Therefore, at least tothe extent that expectations represent intent within theGEM data, expectations should provide a reasonablepredictor of eventual performance16.

Overall, the findings reported above carry importantimplications for policy. Clearly, it seems that growthintentions, and likely also the eventual growth impact, arenot evenly distributed across populations of

entrepreneurial firms. Therefore, to the extent that they arenot already doing so, policy-makers will be well advised tofocus their initiatives on the subset of high-expectationentrepreneurial firms. However, due to absence of data,little is known about who is behind high-expectationentrepreneurial firms. The characteristics of high-expectation entrepreneurs are examined later in this report.

Job Creation Potential by Country and WorldRegionBy combining GEMs adult population survey datasets from2000 to 2004 we are able to examine the job creationpotential by expected size category with a reasonabledegree of accuracy. In Table 5, data is shown for Asia andAfrica. Using per capita income as criterion, Asia has beenfurther divided into highly developed Asia and developingAsia. Highly developed Asia includes Japan, South Korea,and Hong Kong. Developing Asia includes China, ChineseTaipei, India, Jordan, Malaysia, and Thailand. Africaincludes Uganda and South Africa.

The patterns observed for Asia and Africa are quite similarto those reported for the overall GEM dataset. In Africa,high-growth potential nascent entrepreneurs appear lessprevalent than in the two Asian regions. Because of this,almost double the number of nascent entrepreneurs isrequired in Africa for the same expected total of new jobs.The lesser prevalence of high-growth expectations probablyreflects the prevailing conditions in countries such asUganda. On the other hand, expected job creation byAfrican baby businesses follows a similar distribution as inother regions.

Table 4 reveals patterns that are very similar to thoseobserved for nascent entrepreneurial activity. In total,there were 16,485 baby businesses in the GEM 2000 –2004 combined dataset. This represents 4.0 % of the410,402 adult-age individuals in the GEM dataset15. Ofthese, 42.0% expected to employ at most one personwithin five years. Only 27.5 % expected to employ fivepersons or more. Thus, as expected, the job creation

expectations of baby businesses are even moreconservative than those of nascent entrepreneurs.Similarly to nascent entrepreneurs, however, the 10.1% ofbaby businesses, which expected 20 or more jobs, wereresponsible for 73.6% of the total expected job creation.And, the 4.5% who expected 50 or more employees, wereresponsible of 57.8% of the total expected job output infive years’ time.

Table 4 Job Creation Aspirations of Baby Businesses by Size Category

15 Figures are based on weighted data.

16 It should be observed that growth intention does not automatically result in growth in entrepreneurial firms, for much the same reasons why itdoes not happen by accident. Even if an entrepreneurial firm would pursue a growth strategy, achieving employment size growth is not easy.


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Asia Developing Countries: China, Chinese Taipei, India, Jordan, Malaysia, ThailandAsia Highly Developed Countries: Hong Kong, Japan, Singapore, South KoreaAfrica: South Africa, Uganda

Table 5 Job Creation Aspirations of Nascent Entrepreneurs and Baby Businesses by Size Category and World Region:Asia and Africa


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Table 6 shows the distribution of job creation aspirationsby expected size category in North America, LatinAmerica, and Oceania. North America includes Canadaand USA. Latin America includes Argentina, Brazil, Chile,Ecuador, Mexico, Peru, and Venezuela. Oceania includesAustralia and New Zealand.

A similar pattern can be observed for Latin America ascould be observed for Africa in Table 5. In Latin America,a relatively smaller share of nascent entrepreneurs expect

to create many jobs, and the share of high-growthpotential nascent entrepreneurs of total expected jobcreation is relatively smaller in comparison with, e.g.,North America and Oceania. In the case of Latin America,baby businesses also demonstrate relatively smaller jobcreation expectations than in North America or inOceania. Thus, a larger number of nascent entrepreneursin Latin America expect to create significantly less newjobs than a smaller number of nascent entrepreneurs inNorth America.

Table 6 Job Creation Aspirations of Nascent Entrepreneurs and Baby Businesses by Size Category and World Region:North America, Latin America, and Oceania

North America: Canada, USALatin America: Argentina, Brazil, Chile, Ecuador, Mexico, Peru, VenezuelaOceania: Australia, New Zealand


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The pattern observed for Africa and Latin America mayhelp explain observed anomalies in GEM reports ofprevious years. In year 2004, Peru was ranked as the mostentrepreneurial of participating GEM countries. Thisranking was based on the Total Entrepreneurial Activity(TEA) index, which computes the prevalence of nascententrepreneurs and baby businesses in the adult-agepopulation, but does not control their job creationexpectations. If the indices were to include expected jobcreation, the rankings might be different. At present,however, there is little data to analyze what might beinfluencing the observed differences. Variation ineconomic optimism and overconfidence might be onereason. Differences in innovativeness might be another. Athird underlying mechanism might be economic andstructural conditions that prevail in the economies of theregion.

Table 7 shows expected job creation data for Europeancountries. This set includes all European countries thathave participated in GEM during any of the years 2000-2004. For convenience, Israel is treated as a Europeancountry in this report. European countries are furtherdivided into EU large member countries (France,Germany, Italy, Spain, and United Kingdom) and smallEuropean countries (Nordic countries, Belgium,Netherlands, Switzerland, Portugal, and Greece. Israel isalso included in this category, even though it is notformally a part of Europe). Data on new EU membercountries is also reported.

No major differences can be observed for the differentsub-categories of European countries. Overall, Europeandata appears to depict slightly lower growth ambition thanthe North American data, where nascent entrepreneursare concerned. For baby businesses, the distributions arequite similar.


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Europe: Large European countries, small European countries, and EU new member countries, as listed below

Large European Countries: France, Germany, Italy, Spain, UK

Small European Countries: Nordic countries (Denmark, Finland, Iceland, Norway, Sweden), Belgium, Netherlands, Switzerland,Portugal, Greece, Israel

EU New Member Countries: Croatia, Hungary, Poland, Slovenia

Table 7 Job Creation Aspirations of Nascent Entrepreneurs and Baby Businesses by Size Category and World Region:Europe, EU Large Countries, EU Small Countries, and EU New Member Countries


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For some countries, there is sufficient data to providecountry-level comparisons. Table 8 reports data forGermany, Spain, Sweden, United Kingdom, and USA. Oneinteresting observation can be made from this table: inSpain, the prevalence of high-growth activity issignificantly smaller than in the other countries listed.Only approximately 1% of all nascent entrepreneurs and

baby businesses in Spain expect to create 50 or more jobsin five years. For the other countries, the prevalence ofhigh-growth activity varies between 3.9% and 7% fornascent entrepreneurs and baby businesses. Thedifferences compared to Spain are statistically significantand merit closer attention by Spanish policy-makers.

Table 8 Job Creation Aspirations of Nascent Entrepreneurs and Baby Businesses by Size Category and Country:Germany, Spain, Sweden, United Kingdom, and USA

In summary, different world regions and countries depictdifferent relative contributions of high-expectationentrepreneurial activity to job creation. In Africa and LatinAmerica, nascent entrepreneurs and baby businessesdepict smaller job creation expectations than in Asia,Europe, and in North America. In consequence, morenascent entrepreneurs and baby businesses are requiredin these regions for similar job creation potential as in

Asia, Europe, and North America. Of individual countries,Spain stands out as a country where nascententrepreneurs and baby businesses depict smaller growthexpectations than in Germany, Sweden, United Kingdom,and USA. At this point, it is difficult to say what causesthe relatively smaller growth expectations of nascent andnew entrepreneurs in Africa, Latin America, and Spain.


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ConclusionsHigh-expectation entrepreneurial activity represents only asmall proportion of all entrepreneurial activity. Dependingon country and world region, only some 3% to 17% ofnascent entrepreneurs and baby businesses expect toemploy 20 or more employees within five years. Onlysome 1% to 7% of nascent entrepreneurs and babybusinesses expect to employ 50 or more employees withinfive years. There is no systematic difference betweennascent entrepreneurs and baby businesses in terms ofhow growth expectations are distributed within thepopulation. The observed distributions are also consistentwith received studies on the distribution of actual jobgeneration by new firms.

Though rare, high-expectation entrepreneurial activityexplains the bulk of expected new jobs by cohorts ofnascent entrepreneurs and baby businesses. Even thoughhigh-expectation entrepreneurial activity only represents asmall subset of all entrepreneurial activity, its potentialeconomic impact is significant, as nascent and babybusinesses expecting to employ 20 or more employees areresponsible of up to 80% of total expected jobs by allentrepreneurial activity. This is significant, as only some0.2 % – 1.6% of the adult-age population in differentcountries and world regions actively participate in high-expectation entrepreneurial activity.

Because of its potential impact, high-expectationentrepreneurial activity may help balance overallentrepreneurship deficit, as measured by GEMs TotalEntrepreneurial Activity (TEA) index. The distributionsshow, for example, that in highly developed Asiancountries, only half the number of nascent entrepreneursare required to produce a roughly equal number ofexpected jobs as in Africa. This is because nascententrepreneurs in highly developed Asian countries have,on average, greater job creation expectations. Thus, interms of expected job creation, it is not only the overallTEA rate that matters – it is also the quality of the overallentrepreneurial activity, as measured by growthexpectations.

Governments should be aware of the importance of high-expectation and high-potential entrepreneurial activity andconsider introducing highly selective support measuresand policies. To the extent to which the goal ofgovernment policy is to support job creation throughentrepreneurial activity, governments should be aware ofthe highly skewed distribution of job creation expectationswithin populations of nascent and baby businesses. Ifonly a small percentage of all firms promise to deliver thebulk of new jobs, then unfocused measures may not be aseffective as measures that are specifically targeted at high-potential ventures. To achieve better selectiveness,governments should study who is behind high-potentialentrepreneurial activity, and how this kind of activity couldbe supported. Given the skewness of the distributions

reported here, highly selective policy measures couldprove more effective for job creation purposes than non-selective ones.

More research is required to learn who is behind high-expectation entrepreneurial activity. Little is known aboutwho is behind high-expectation nascent andentrepreneurial businesses. Some findings will bereported later in this report. Given the potentialimportance of this group for job creation, more empiricalresearch on the topic is necessary.

Who is Behind High-Expectation Entrepreneurial Activity?


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Who starts high-expectation new firms? In order tounderstand the implications of high-expectation activityfor government policy, it is important to know whetherhigh-expectation entrepreneurial activity differs frommainstream entrepreneurial activity in any way. For policyconclusions, tangible, observable, and meaningfuldifferences between high-expectation and low-expectationentrepreneurial activity needs to be established. In thefollowing, we use both demographic descriptors fromGEMs adult population survey, as well as data describingthe entrepreneurial activity observed, to study differencesbetween high- and low-expectation entrepreneurs, bothglobally and within Europe.

Global Differences Between High-Expectation and Low-Expectation Nascentand Baby BusinessesThe GEM dataset contains rich demographic data aboutindividual respondents, including gender, age, educationlevel, household income, and employment status. Thisdata is complemented by interview data on therespondent’s motivations, attitudes, and entrepreneurialactivities. The dataset also contains industry sector dataon the nascent and baby businesses. Statisticallysignificant, and meaningful, differences between high- andlow-expectation nascent and baby businesses are shownin figures 7 -18.

Figure 7 shows statistically significant differences betweenhigh- and low-expectation nascent entrepreneurs in termsof age and gender in the global GEM sample17. High-expectation nascent entrepreneurs are significantly morelikely to be male and from 18 to 24 years old than low-expectation nascent entrepreneurs. Two of the older agecategories, 25 – 34 years and 45 – 54 years, depictdisproportional prevalence of low-expectationentrepreneurs in the global GEM sample.

Figure 8 shows demographic differences for babybusinesses in the global sample. As in the case of nascententrepreneurs, young males are over-represented amonghigh-expectation baby businesses: some 50% of high-expectation baby business managers belong to the agecategory of 25 – 34 years. For ages 45 years and above,low-expectation baby business managers are over-represented. Thus, expectations for rapid growth amongbaby business managers appear to decline fairlyconstantly as a function of age.

Figure 7 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: OverallSample – Demographic Characteristics

Figure 8 Comparison of High-Expectation and Low-Expectation Baby Businesses: Overall Sample –Demographic Characteristics

17 Note that the global GEM sample has been adjusted according to population size. Because there are two very large countries in the sample, Chinaand India, the global comparison is dominated by these two countries.

* p <0.05 ** p <0.01 *** p <0.001

* p <0.05 ** p <0.01 *** p <0.001


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In terms of work status, high-expectation nascententrepreneurs are over-represented among those whohave either full or part-time work (Figure 9). However, themajority of both high- and low-expectation nascententrepreneurial activity is associated with individuals witha full- or part-time employment status. Among those whoare not working, low-expectation activity dominatesamong nascent entrepreneurs. For baby businesses, onlyone significant difference is observed: low-expectationbaby business managers are more likely to be employedfull- or part-time. Note however, that the magnitude ofthis difference is only four percentage points, so thepractical significance of this difference is minor.

The over-representation of high-expectation activityamong nascent entrepreneurs of full- or part-time workstatus may be due to at least two reasons. First, startingan entrepreneurial firm represents a significantemployment trade-off for those who already have a job, interms of job and income security. A part of the differencebetween high- and low-expectation activities may thus becaused by the need to compensate for this trade-off withhigher earning expectations associated with fasteremployment growth. Second, individuals in activeemployment are more likely to develop the skills andsocial capital necessary to pursue business opportunities.This could also help to explain part of the over-representation of high-expectation activity among thosealready employed.

Figure 9 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Overall Sample – EmploymentStatus

Figure 10 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Overall Sample – EmploymentStatus

* p <0.05 ** p <0.01 *** p <0.001

* p <0.05 ** p <0.01 *** p <0.001


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Differences in terms of income level and education mayalso be suggestive of the trade-offs involved in choosing acareer in entrepreneurship. As shown in Figures 11-12,high-expectation entrepreneurial activity is over-represented in high-income groups as well as among well-educated individuals. Among nascent entrepreneurs, over72% of high-expectation activity occurs among individualswho belong to the highest third in the household incomesegment. This percentage is virtually the same for babybusiness mangers. Of low-expectation nascententrepreneurs, only 33% belong to this segment, and forbaby business managers, this percentage drops to 21.4%.Of low-expectation baby business managers, 40.1%belongs to the lowest third household income category.Similarly, nearly 38% of high-expectation nascententrepreneurs and 26% of high-expectation baby businessmanagers have completed a post secondary or highereducation degree, against 20% of low-expectationentrepreneurs.

These patterns may be suggestive of both trade-offs aswell as human and social capital effects. Individuals witha higher income level may not jump on low-expectationentrepreneurial opportunities. It may be that a highergrowth expectation is required to make the trade-offbetween secure high income and more insecureentrepreneurial income attractive for a high-incomeindividual. This explanation could apply more to nascententrepreneurial activity. Because GEM measures incomeas household income, it may also be that individuals fromhigh-income families may be exposed to more lucrativeopportunities than individuals from low-income families –this would be a social capital effect on opportunityexposure. It is also possible that a high household incomeand high human capital go hand-in-hand, and the higherhuman capital of high-income individuals could thusexplain part of the observed difference. This would beconsistent with the observation that high-expectationentrepreneurial activity is more in evidence among welleducated individuals, both for nascent and for babybusiness entrepreneurial activity. Finally, high humancapital itself (enhanced by education) may boost growthexpectations because many important entrepreneurialskills (such as visioning, analysis, imagination, searchskills) are enhanced by education (Sapienza & Grimm,1997; Barringer, Jones, & Neubaum, 2005). Because ofthe multiplicity of potential effects, more research anddata is needed to assess the pertinence of these, andother, potential explanations.

Figure 11 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Global Sample – Income Leveland Education

Figure 12 Comparison of High-Expectation and Low-Expectation Baby Businesses: Global Sample – Income Level andEducation

* p <0.05 ** p <0.01 *** p <0.001

* p <0.05 ** p <0.01 *** p <0.001


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High-expectation entrepreneurs differ from low-expectation ones also in terms of their entrepreneurialactivities and attitudes. As shown in Figure 13, high-expectation nascent entrepreneurs are significantly morelikely to perceive good business opportunities, they aremore likely to expect to start a business within threeyears, they are more likely to know people who havestarted a business, and they are almost twice as likely tohave invested in entrepreneurial ventures started byothers than are low-expectation nascent entrepreneurs.High-expectation nascent entrepreneurs are less likely tohave discontinued a business during the past year,however. Thus, exposure to other entrepreneurs, informalinvestor activity, and greater entrepreneurial activity ingeneral is associated with higher growth expectations.

Similar patterns can also be observed for baby businesses(Figure 14). In particular, high-expectation baby businessmanagers are more likely to perceive good opportunities,to expect to start new businesses in the future, and topersonally know other entrepreneurs than are low-expectation baby business managers. High-expectationbaby business managers are nearly four times as likely tohave invested in new firms started by others than are low-expectation baby business managers. Contrary to high-expectation nascent entrepreneurs, high-expectation babybusiness managers are also significantly more likely thanlow-expectation baby business managers to havediscontinued a business in the past 12 months.

The patterns observed in Figure 13 may be indicative ofthe importance of general entrepreneurial exposure forhigh-growth expectations. The more a baby businessmanager has had exposure to entrepreneurial activitiessuch as informal investor activity, familiarity with otherentrepreneurs, or future expectations to actentrepreneurially, the greater the growth expectations theyappear to depict. More research is required to confirm thevalidity of this pattern, but even now, the data suggeststhat greater exposure to entrepreneurial activity may feedhigh-growth expectations.

Figure 13 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Global Sample – EntrepreneurialActivities

* p <0.05 ** p <0.01 *** p <0.001

Figure 14 Comparison of High-Expectation and Low-Expectation Baby Businesses: Global Sample – EntrepreneurialActivities

* p <0.05 ** p <0.01 *** p <0.001


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High- and low-expectation entrepreneurial activity is alsodifferentially present in different business sectors. Asshown in Figure 15, high-expectation nascententrepreneurial activity is relatively more prevalent in the:manufacturing; mining and construction; businessservices; financial services; and health, educational, andsocial services sectors. Low-expectation nascententrepreneurial activity is relatively more prevalent in theretail trade, hotels, and restaurants; as well as agriculture,forestry, and fishing sectors. It is interesting that thesingle largest sector for nascent entrepreneurial activity –retail trade, hotels and restaurants – also shows thelowest prevalence of high-growth expectations, ascompared to low-expectation activity.

For baby businesses, the differences are broadly similar.High-expectation baby business activity is more prevalentin the: manufacturing; business services; wholesale andmotor vehicle sale; transportation, communication, andutilities; and mining and construction sectors. Similarly tonascent entrepreneurial activity, the sector with a highestoverall level of entrepreneurial activity depicts a relativeunder-representation of high-expectation activity.

Sector differences such as those reported here might help explain some of the observed differences betweencountries in terms of Total Entrepreneurial Activity (TEA).In previous years, GEM has reported very high rates ofTEA for countries, such as Uganda, that are not generallyperceived as highly innovative. It may be that Uganda’seconomy is dominated by sectors, such as retail trade,hotels, and restaurants; and agriculture, which may offergood opportunities for small firm creation but alsoexperience low levels of high-expectation entrepreneurialactivity. For Nordic countries, where very similarinstitutions such as the social security system exist,Norway and Denmark customarily post higher TEA ratesthan do Sweden and Finland. Finland and Sweden bothhave a high emphasis on mechanical engineering sectors,as well as pulp and paper industries, which may not havehigh levels of entrepreneurial activity due to the existenceof high barriers to entry. Clearly, interpreting TEA rates forindividual countries requires some attention to theprevalence of different industry sectors in the economy, aswell as to the prevalence of high-expectationentrepreneurial activity in different sectors.

Figure 15 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Global Sample – IndustrySectors

Figure 16 Comparison of High-Expectation and Low-Expectation Baby Businesses: Global Sample – Industry Sectors

* p <0.05 ** p <0.01 *** p <0.001

* p <0.05 ** p <0.01 *** p <0.001


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Finally, motivational characteristics of high-expectationand low-expectation entrepreneurial activity werecompared. Figure 17 shows the comparison for nascententrepreneurs. As can be seen, high-expectation nascententrepreneurs are more likely to believe that they havethe necessary skills to start a new business; are moredriven by opportnity motivation18; and are less likely tobe constrained by fear of failure. The difference betweenhigh- and low-expectation nascent entrepreneurs isparticularly dramatic for necessity motivation: low-expectation nascent entrepreneurs were over four timesas likely to indicate a necessity motivation as were high-expectation nascent entrepreneurs.

The motivational characteristics of baby businessmanagers are shown in Figure 18. The pattern is verysimilar to that observed for nascent entrepreneurs.The differences are particularly important foropportunity and necessity motivations.

18 Opportunity and necessity motivation is inquired by GEM with the question: “Are you starting this business to pursue an opportunity or becauseyou have no other way of making a living?”. Opportunity pursuit is labelled as opportunity motivation, and ‘no other way of making a living’ islabelled as necessity motivation.

Figure 17 Comparison of High-Expectation and Low-Expectation Nascent Entrepreneurs: Global Sample – EntrepreneurialMotivation

Figure 18 Comparison of High-Expectation and Low-Expectation Baby Businesses: Global Sample – EntrepreneurialMotivation

* p <0.05 ** p <0.01 *** p <0.001

* p <0.05 ** p <0.01 *** p <0.001


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Above, we have reviewed differences between high- and low-expectation nascent and baby businesses using the combinedGEM 2000 – 2004 sample as the basis of our analysis. Theoverall GEM 2000 – 2004 dataset has been harmonized so asto provide a representative picture of all countries included inthe overall dataset. The data has thus been adjusted forsample and population size. Because there are two very largecountries in the sample – China and India – the globalcomparison may be said to be more reflective of developingAsian countries than, say, European countries. In thefollowing, we restrict the comparison to European data alone.Meaningful comparisons between high- and low-expectationentrepreneurial activity is possible in the European databecause the GEM dataset contains over 265,000 interviewsundertaken in various European countries.

Table 9 presents comparisons between high- and low-expectation nascent entrepreneurs in the European data.Only statistically significant, and meaningful, differences areshown19. As can be seen, the distributions are broadly similar

to the global comparison, with some differences, however.First, the age category of 25 – 34 years witnesses over-representation of low-expectation nascent entrepreneurs,whereas the age category 35 – 44 years witnesses an over-representation of high-expectation nascent entrepreneurs. This difference, while statisticallysignificant, is not very big, however. Second, the highest third in the household income category witnesses an over-representation of high-expectation activity, but this over-representation is not as significant as in the globalcomparison. Third, education displays a similar pattern as in the global comparison, but the differences are lessdramatic. The patterns observed for entrepreneurial activity variables are broadly similar to those observed for the global comparison, as is data concerning entrepreneurial motivations. In Europe, business services represent a greater proportion of both high- and low-expectation entrepreneurial activity than it does in the global comparison.

Table 9 Differences between High-Expectation and Low-Expectation Nascent Entrepreneurs – Europe

19 Because of the size of the dataset, a difference can be statistically significant without really being meaningful in practice.

Differences between High-Expectation and Low-Expectation Nascent and Baby Businesses –Europe

* p <0.05

** p <0.01

*** p <0.001


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European baby business managers depict similar patternsto those above. The age differences between nascent andbaby business entrepreneurs are not major.Entrepreneurial activity also depicts similar patterns,except that informal investor activity does not differentiatebetween European high-expectation and low-expectationbaby business managers. As elsewhere, the greatestpercentage difference can be seen for household income,with higher-income baby business managers displayingsignificantly higher growth expectations. Also, opportunityand necessity motivation display similar patterns to thosein the global comparison. However, it is noteworthy thatnecessity motivation is not as significant a differentiatorin Europe as it is in the global comparison.

Overall, the patterns displayed for European comparisonsare very similar to the global comparison, even though thepercentage differences appear, in general, to be smaller inEurope. This holds particularly for income, education,work status, and opportunity motivation variables. Onemay speculate that the generally smaller percentagedifferences in Europe may reflect the generally smallerincome, educational, and social disparities in Europe thanin many other parts of the world. As such disparities arereduced, one may speculate that activity variables (e.g.,familiarity with entrepreneurs, informal investor activity)and intrinsic motivations could become more importantdeterminants of high-growth expectations.

Table 10 Differences between High-Expectation and Low-Expectation Baby Businesses – Europe

* p <0.05

** p <0.01

*** p <0.001


As can be seen, high-income, well educated malesdisplay significantly higher prevalence rates of high-expectation nascent entrepreneurial activity than doesthe overall population. In the global comparison, high-income, well educated males of 25 – 34 years were fourtimes as likely to be involved in high-expectation activityas the adult-age population in general. For the agecategory of 35 to 44 years, high-income, well educatedmales were five times as likely as the overall populationto be involved in high-expectation nascententrepreneurial activity.

For baby business managers, in the globalcomparison, equally important prevalence rates forhigh-expectation activity can be observed (Figure20). In the age category of 25 to 34 years, high-income, well educated males were ten times morelikely than the overall population to be involved inhigh-expectation activity. The participation rate inhigh-expectation activity for this cell, 4.4%, is thehighest observed for any cell defined using basicdemographic variables. In the age category of 35 to44 years, they were five times more likely than thegeneral population to be involved in high-expectation activity.

The data suggests higher prevalence rates in some sub-cells of the adult-age population. Pertinent demographicvariables appear to be income, education, gender, andage. Based on our analysis so far, we can expect welleducated, high-income males to display particularly highprevalence rates of high-expectation entrepreneurialactivity, as compared to the overall population. Toexamine just how high the rates might be in this cell, weisolated high-income, well educated males from the rest

of the population and checked how the high-expectationprevalence rates of these compare with the overallpopulation in different age categories. High income wasdefined as the individual belonging to the top thirdhousehold income category. High education was definedas the individual possessing a secondary education orhigher degree. We carried out separate analyses for theGEM 2000 – 2004 global sample, as well as for the GEM2000 – 2004 Europe sample.

Figure 19 Prevalence Rates of Male, Well Educated, High-Income Nascent Entrepreneurs in Different Age Categories –GEM Global Sample

Prevalence Rates Among Well Educated, High-Income Males

* p <0.05 ** p <0.01 *** p <0.001

Figure 20 Prevalence Rates of Male, Well Educated, High-Income Baby Business Managers in Different Age Categories –GEM Global Sample

* p <0.05 ** p <0.01 *** p <0.001

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We also carried out the same analysis in the EuropeanGEM sample, with similar results. As shown in Figure21, high-income, well educated males were two tofour times as likely as the general population to beinvolved in high-expectation nascent activity indifferent age categories. Also, they were two to sixtimes as likely as the general adult-age population tobe involved with high-expectation baby businesses.Thus, significant differences exist between differentpopulation cells (as defined using basic demographicvariables) in terms of participation in high-expectationentrepreneurial activity.

How important are these cells? High-income, welleducated males of the 25 – 34 year age categoryonly represent 1.3% of the adult-age population inthe GEM countries. Yet, they are responsible for13.6% of the overall high-expectation baby businessactivity in the GEM sample. Combined, the threeage cells in this education-income-gender category(18 – 44 years) represent 7.7% of the adult-agepopulation and 22% of all high-expectation babybusiness activity. High-expectation activity is thusnot evenly distributed within adult-age population,and a major portion of all expected job creationappears to be concentrated in relatively few high-prevalence population cells. When one combinesthis information with the finding that only a smallproportion of all new firms are responsible for themajority of all new jobs created through theentrepreneurial process, the importance of studyingthe occurrence of high-expectation activity withinsub-cells of the overall population becomes obvious.One has to remember, however, that even this high-prevalence cell leaves nearly 80% of the high-expectation activity unexplained.

Figure 21 Prevalence Rates of Male, Well Educated, High-Income Nascent Entrepreneurs in Different Age Categories –GEM Europe Sample

Figure 22 Prevalence Rates of Male, Well Educated, High-Income Baby Business Managers in Different Age Categories –GEM Europe Sample

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* p <0.05 ** p <0.01 *** p <0.001

* p <0.05 ** p <0.01 *** p <0.001


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ConclusionsHigh-expectation nascent and baby businessentrepreneurs differ from the population of low-expectation entrepreneurs in many respects. The analysisshows several important differences between high-expectation and low-expectation nascent entrepreneursand baby business managers. Most of the differencesobserved are either based on entrepreneurial attitudesand beliefs, or on objectively observable demographicdifferences, such as gender, age, education, and income.Our analysis thus provides pointers for policy-makers, asthey strive to understand the drivers of high-expectationentrepreneurial activity and design associated supportmeasures.

High household income, high education level, andopportunity motivation are most strongly associated withhigh-growth expectations. The greatest distinguishingelements for high-expectation entrepreneurial activitycould be observed for income, education, and opportunitymotivation. These are suggestive of the individual-leveleconomic trade-offs related to the entrepreneurialdecision.

Entrepreneurial activity, perhaps even serialentrepreneurial activity, is associated with high-growthexpectations. Overall, the more active a given individualwas in various kinds of entrepreneurial activity (e.g.,making informal investments to start-ups by others,identifying opportunities, knowing other entrepreneurs, ordiscontinuing a business), the more likely that individualwas to be engaged in high-expectation entrepreneurialactivity. This suggests that cultivating serialentrepreneurs, as well as increasing the population’sgeneral exposure to entrepreneurial activity, might begood ways to foster high-expectation entrepreneurialactivity.

High-growth expectations peak at young to middle age.Both young and middle-aged age categories tend to havehigher growth expectations than older categories. Thehighest prevalence rates could be observed for the agecategory 25 – 34 years.

Population cells differ significantly in terms of high-expectation entrepreneurial activity. The highestprevalence observed for a single population cell (definedusing basic demographic characteristics) was ten timeshigher than the population-wide participation rate in high-expectation entrepreneurial activity. Of baby businessmanagers, 25-34 year olds, high-income and welleducated males displayed a 4.4% participation rate inhigh-expectation activity – meaning that nearly one intwenty individuals in this population cell are activelyengaged with high-expectation entrepreneurial start-ups.

Industry sectors differ significantly in terms of the relativeprevalence of high-expectation entrepreneurial activity.Highest relative prevalence rates of high-expectationentrepreneurial activity can be seen in manufacturingsectors, as well as in business services. The lowestrelative prevalence rates of high-expectationentrepreneurial activity can be seen in retail trade andhotel and restaurant business. Thus, the sector with thehighest overall prevalence of entrepreneurial activity alsowitnessed the highest relative prevalence of low-expectation activity.

While Europe displays similar overall patterns, thepercentage differences between high- and low-expectationentrepreneurs tended to be smaller. This may be a sign ofthe influence of the generally smaller variation in income,education, and economic trade-offs in Europe, ascompared with many other parts of the world.

Economic trade-offs may play an important role inexplaining high-growth expectations. The analysissuggests that the higher prevalence rate of high-expectation entrepreneurial activity among high-income,well educated individuals may be partly explained byeconomic trade-offs related to the entrepreneurialdecision. From one perspective, the decision to embarkupon an entrepreneurial career involves a trade-offbetween job and income security, on the one hand, anduncertain expectations of higher earnings, on the other. Ifthis is true, then addressing such trade-offs provides animportant challenge for entrepreneurship policy.


41

How does high-expectation entrepreneurial activity relateto national entrepreneurial framework conditions? Dorelationships exist between favorable conditions and high-expectation entrepreneurial activity? One important aspectof GEM is the annual compilation of survey data fromamong national experts knowledgeable of the nationalenvironment for entrepreneurial activity20. Using thisdata, indicators of national framework conditions arecomputed. National framework conditions are aspects ofthe national economic and social environment thatdirectly interact with entrepreneurial activity. GEMdistinguishes between 16 such framework conditions.These are: (1) availability of debt and equity funding fornew and growing firms; (2) government policy prioritygiven to new and growing firms; (3) functioning ofgovernment regulations and institutions; (4) governmentsupport programs targeted at new and growing firms; (5)education system support for entrepreneurial skills andattitudes; (6) technology transfer to new and growingfirms; (7) availability of business and support services; (8)market change and dynamism; (9) market openness tonew and growing firms; (10) quality of physicalinfrastructure; (11) entrepreneurial aspects of nationalculture; (12) presence of opportunities to start new andgrowing firms; (13) prevalence of entrepreneurial skillsand competencies within the adult-age population; (14)social respect for entrepreneurs and entrepreneurialsuccess; (15) protection of intellectual property rights;and (16) support for womens entrepreneurial activity21.Each of these indicators is computed as a multi-itemscale, with higher values indicting better quality of theframework condition22.

An examination of the relationships between high-expectation entrepreneurial activity and the quality ofnational framework conditions is interesting from theperspective of identifying national framework conditionsthat might be associated with high-prevalenceentrepreneurial activity. In the following, we report simplebivariate correlations between national entrepreneurial

framework conditions and a set of variables that describethe prevalence of various kinds of entrepreneurialactivities within the national economy. These variablesare: (1) high-expectation entrepreneurial activity (bothnascent and baby business activity, with two levels ofgrowth expectations); (2) Total Entrepreneurial Activity(TEA) rate; (3) opportunity-driven TEA rate23;(4) necessity-driven TEA rate24; (5) prevalence of informalinvestor activity within the adult-age population25; and (6) population-level familiarity with entrepreneurs26.

Two sets of bivariate correlations are presented. First, wecorrelate population-level prevalence of high-expectationentrepreneurial activity against national entrepreneurialframework conditions. Population-level prevalenceindicates the share of adult-age population that iscurrently involved with high-expectation nascent or babybusiness activity. Second, we correlate the share of high-expectation entrepreneurial activity of the total nationalentrepreneurial activity against the same variables. The firstset of correlations shows how national entrepreneurialframework conditions relate to the overall prevalence rateof high-expectation activity. The second set of correlationsshows how the anatomy of entrepreneurial activity relateswith national conditions.

In the regressions, pooled data from years 2000 to 2004 isused. Each country and year represents one observationin the dataset.

Table 11 shows the bivariate correlations with population-level high-expectation entrepreneurial activity. Severalinteresting observations can be made from this simple setof correlations. First, the various aspects of national-levelentrepreneurial activity (high-expectation entrepreneurialactivity rates, TEA rates, TEA opportunity and necessity,and informal investor activity rate) are positivelycorrelated. Thus, a higher overall prevalence ofentrepreneurial activities in a nation in general isassociated with a higher overall prevalence of high-expectation activity. As regards informal investor activity,

National Entrepreneurial Framework Conditions and High-ExpectationEntrepreneurship

20 Remember that GEM uses three types of data: (1) annual adult population surveys; (2) annual surveys of national experts; and (3) third-party datadescribing national economy and demographic and societal conditions. Here we refer to the national expert survey data, which is compiled in orderto assess countries’ ‘national entrepreneurial framework conditions’.

21 The 16 ‘entrepreneurial framework conditions’ have been chosen on the basis of extensive reviews of received research and literature onentrepreneurship, as well as on the basis of extensive discussion and debate within the GEM consortium over several years. They represent the 16national conditions that are, collectively by GEM consortium teams, considered to be of the greatest direct relevance for national-levelentrepreneurial activity.

22 At least five statements are used to assess each framework condition. For each condition, the statement scores load on a single factor, and theinternal reliability of each multi-item scale is high. The final indicator of the quality of each framework condition is computed as weighted mean ofindividual statement scores, using rotated factor loadings of individual statement scores as weights. For detailed account of the GEM methodregarding national framework conditions, please refer to Reynolds, Bosma, Autio, et al (2005).

23 Opportunity-driven TEA rate represents the part of Total Entrepreneurial Activity which responds to business opportunities.24 Necessity-driven TEA rate represents the part of Total Entrepreneurial Activity which is initiated due to the absence of other ways of making a living.25 The share of adult-age population that have invested in new firms started by others during the past three years.26 The share of adult-age population indicating that they personally know someone who has started and runs a new firm.


42

it is noteworthy that the correlations tend to be strongerwith the overall TEA rate, as well as with TEA opportunityand necessity, than with high-expectation activity. In theGEM data, an individual is categorized as an informalinvestor if (s)he has invested his or her own funds, duringthe past three years, into a new entrepreneurial firmstarted by someone else. Most of the informalinvestments in the GEM dataset are made in companiesstarted by relatives and friends; less than 10% arebusiness angel investments.

Another interesting pattern in Table 11 concerns therelationships between high-expectation entrepreneurialactivity and national framework conditions, as comparedwith those depicted by the various TEA rates. Overall, thecorrelations between high-expectation baby businessactivity and national framework conditions tend to beneutral or positive, indicating a positive link between thetwo. Significant positive correlations are indicated fornational entrepreneurial culture; respect forentrepreneurs; and government regulations andinstitutions. The more favorable these were, the higher theprevalence rate of high-expectation baby business activity.For high-expectation nascent activity, some negativecorrelations can also be observed. Negative, andstatistically significant, correlations are observed foravailability of funding; government policies; andgovernment programs.

When one looks at the general TEA rates, which consistmainly of low-expectation activity, the pattern of negativecorrelations is strengthened. The strongest and mostconsistent negative correlations can be observed for theTEA (necessity) rate and national entrepreneurialframework conditions. These correlations may be a sign ofthe generally higher prevalence of generic entrepreneurialactivity in lower-income countries (see, e.g., Acs et al,2005). Thus, it may be that both national frameworkconditions and general prevalence rate of entrepreneurialactivity of any kind are higher in low-income countries.

It is also noteworthy that, where the overall pattern ofcorrelations between high-expectation baby businessactivity and framework conditions tends to be positive,the general pattern of TEA (necessity) is negative.Different forms of entrepreneurial activity, therefore, aredifferentially related to national framework conditions.This is suggestive of discriminant validity for various GEMindicators of entrepreneurial activity. Because therelationships work in opposite directions, there is apossibility that they may also respond differentially topolicy interventions.


43

Bab

ybu

sine

ss50

+0.

920*

**

Nas

cent

entr

epre

neur

20+

0.70

1***

0.62

4***

Nas

cent

entr

epre

neur

50+

0.71

8***

0.71

0***

0.85

3***

TEA

rate

0.50

9***

0.41

7***

0.67

4***

0.56

7***

TEA

(opp

ortu

nity

)ra

te0.

454*

**0.

374*

**0.

657*

**0.

575*

**0.

947*

**

TEA

(nec

essi

ty)

rate

0.44

3***

0.36

5***

0.54

0***

0.42

4***

0.87

0***

0.67

5***

Bus

ines

san

gela

ctiv

ity0.

260*

*0.

234*

*0.

345*

**0.

276*

*0.

535*

**0.

583*

**0.

450*

**

Fam

iliar

ityw

ithen

trep

rene

urs

0.12

70.

131

0.18

5*0.

173*

0.20

9*0.

304*

**0.

215*

0.77

7***

Ava

ilabi

lity

offu

ndin

g0.

015

0.02

0-0

.188

*-0

.092

-0.3

66**

*-0

.223

*-0

.499

***

-0.2

97**

*-0

269*

*

Gov

ernm

ent

polic

ies

0.02

10.

064

-0.2

24**

-0.1

11-0

.346

***

-0.2

16*

-0.4

31**

*-0

.161

-0.1

56

Gov

ernm

ent

regu

latio

nsan

din

stitu

tions

0.22

6**

0.23

0**

0.05

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087

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280.

005

-0.2

74**

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062

Gov

ernm

ent

prog

ram

s0.

013

0.03

0-0

.230

**-0

.159

-0.4

07**

*-0

.273

**-0

.537

***

-0.1

38-0

.065

Educ

atio

nsy

stem

s0.

205*

0.14

00.

154

0.09

1-0

.088

-0.0

14-0

.243

**0.

030

0.06

9

Tech

nolo

gytr

ansf

er0.

086

0.08

5-0

.155

-0.0

62-0

.375

***

-0.2

52**

-0.4

88**

*-0

.082

-0.0

10

Bus

ines

sse

rvic

es0.

047

0.00

10.

024

-0.0

39-0

.174

*-0

.004

-0.3

30**

*0.

023

0.08

3

Mar

ket

chan

ge0.

081

0.16

20.

027

0.14

70.

046

-0.0

560.

171

-0.0

13-0

.030

Mar

ket

open

ness

0.21

5*0.

197*

0.04

40.

028

-0.1

570.

024

-0.3

05**

*0.

010

0.01

2

Phys

ical

infr

astr

uctu

re0.

161

0.11

50.

143

0.07

5-0

.157

-0.0

82-0

.357

***

0.05

90.

041

Nat

iona

lent

repr

eneu

rial

cultu

re0.

320*

**0.

290*

**0.

186*

0.24

1**

0.14

60.

181

-0.0

370.

030

-0.1

09

Ava

ilabi

lity

ofop

port

uniti

es0.

198*

0.16

0-0

.027

-0.0

170.

053

0.20

2*-0

.097

0.12

80.

062

Popu

latio

nen

trep

rene

uria

lcom

pete

nce

0.23

9**

0.19

8*0.

167*

0.17

0*0.

119

0.18

8*-0

.038

0.08

90.

080

Res

pect

for

entr

epre

neur

s0.

359*

**0.

317*

**0.

087

0.12

30.

134

0.15

70.

125

0.13

00.

026

Inte

llect

ualp

rope

rty

prot

ectio

n0.

011

0.08

3-0

.122

-0.0

84-0

.436

***

-0.2

43**

-0.6

32**

*-0

.141

-0.0

48

Supp

ort

for

wom

enen

trep

rene

ursh

ip0.

180

0.20

60.

113

0.17

2-0

.049

0.05

2-0

.187

0.13

20.

210*

***

Cor

rela

tion

issi

gnifi

cant

atth

e0.

001

leve

l(2-

taile

d)**

Cor

rela

tion

issi

gnifi

cant

atth

e0.

01le

vel(

2-ta

iled)

*C

orre

latio

nis

sign

ifica

ntat

the

0.05

leve

l(2-

taile

d)

Tabl

e11

Cor

rela

tions

betw

een

Nat

iona

lEnt

repr

eneu

rial

Fram

ewor

kC

ondi

tions

and

the

Popu

latio

nPr

eval

ence

ofH

igh-

Expe

ctat

ion

Entr

epre

neur

ialA

ctiv

ity

Familiarity withentrepreneurs

Business angelactivity

TEA (necessity)rate

TEA (opportunity)rate

TEA rate

Nascententrepreneur 50+


Baby business 50+

Baby business 20+


44

Overall, however, the correlations in Table 11 for high-expectation entrepreneurial activity and nationalconditions appear rather neutral. If the nationalentrepreneurial framework conditions are to have aninfluence on the prevalence of high-expectation activity,

stronger and more consistent correlations would havebeen expected27. Also, the overall negative correlationpattern observed for the generic forms of entrepreneurialactivity may raise questions among policy-makers.

27 More sophisticated tests than are possible to report here, using cross-sectional panel regression techniques and strong theory, are needed to sortout the relationships.

Bab

ybu

sine

ss50

+0.

782*

**

Nas

cent

entr

epre

neur

20+

0.65

1***

0.63

1***

Nas

cent

entr

epre

neur

50+

0.51

2***

0.55

8***

0.84

0***

TEA

rate

-0.1

41-0

.116

-0.1

51-0

.107

TEA

(opp

ortu

nity

)ra

te-0

.169

-0.1

13-0

.159

-0.0

880.

947*

**

TEA

(nec

essi

ty)

rate

-0.2

32*

-0.1

62-0

.190

*-0

.133

0.87

0***

0.67

5***

Bus

ines

san

gela

ctiv

ity-0

.095

-0.0

54-0

.022

0.01

30.

535*

**0.

583*

**0.

450*

**

Fam

iliar

ityw

ithen

trep

rene

urs

-0.0

64-0

.047

0.01

30.

071

0.20

9*0.

304*

**0.

215*

0.77

7***

Ava

ilabi

lity

offu

ndin

g0.

332*

**0.

281*

**0.

211*

0.20

2*-0

.366

***

-0.2

23*

-0.4

99**

*-0

.297

***

-0.2

69**

Gov

ernm

ent

polic

ies

0.24

2**

0.18

9*0.

082

0.14

6-0

.346

***

-0.2

16*

-0.4

31**

*-0

.161

-0.1

56

Gov

ernm

ent

regu

latio

nsan

din

stitu

tions

0.31

8***

0.27

1**

0.32

3***

0.26

6**

-0.1

280.

005

-0.2

74**

0.10

30.

062

Gov

ernm

ent

prog

ram

s0.

320*

**0.

232*

*0.

238*

*0.

206*

-0.4

07**

*-0

.273

**-0

.537

***

-0.1

38-0

.065

DSU

1000

40.

452*

**0.

322*

*0.

291*

*0.

215*

-0.1

32-0

.016

-0.2

83**

0.01

10.

097

DSU

2000

40.

346*

**0.

218*

0.23

9*0.

124

-0.1

04-0

.045

-0.1

89-0

.127

-0.1

54

dsum

0004

0.55

7***

0.43

4**

0.37

6**

0.17

4-0

.127

-0.0

10-0

.298

0.17

00.

167

Educ

atio

nsy

stem

s0.

488*

**0.

349*

**0.

341*

**0.

202*

-0.0

88-0

.014

-0.2

43**

0.03

00.

069

Tech

nolo

gytr

ansf

er0.

298*

**0.

212*

0.18

7*0.

223*

*-0

.375

***

-0.2

52**

-0.4

88**

*-0

.082

-0.0

10

Bus

ines

sse

rvic

es0.

326*

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*0.

241*

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-0.1

74*

-0.0

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0.02

30.

083

Mar

ket

chan

ge-0

.110

0.03

40.

072

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046

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560.

171

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Mar

ket

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ness

0.31

8***

0.27

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0*0.

200*

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570.

024

-0.3

05**

*0.

010

0.01

2

Phys

ical

infr

astr

uctu

re0.

375*

**0.

264*

*0.

368*

**0.

212*

-0.1

57-0

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-0.3

57**

*0.

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0.04

1

Nat

iona

lent

repr

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cultu

re0.

280*

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291*

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60.

181

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370.

030

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09

Ava

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lity

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port

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185*

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0.09

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0.12

80.

062

Popu

latio

nen

trep

rene

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lcom

pete

nce

0.18

6*0.

209*

0.13

50.

157

0.11

90.

188*

-0.0

380.

089

0.08

0

Res

pect

for

entr

epre

neur

s0.

302*

**0.

270*

*0.

126

0.13

40.

134

0.15

70.

125

0.13

00.

026

Inte

llect

ualp

rope

rty

prot

ectio

n0.

427*

**0.

382*

**0.

313*

**0.

246*

*-0

.436

***

0.24

3**

-0.6

32**

*-0

.141

-0.0

48

Supp

ort

for

wom

enen

trep

rene

ursh

ip0.

243*

0.24

8*0.

333*

*0.

305*

*-0

.049

0.05

2-0

.187

0.13

20.

210*

***

Cor

rela

tion

issi

gnifi

cant

atth

e0.

001

leve

l(2-

taile

d)**

Cor

rela

tion

issi

gnifi

cant

atth

e0.

01le

vel(

2-ta

iled)

*C

orre

latio

nis

sign

ifica

ntat

the

0.05

leve

l(2-

taile

d)

Tabl

e12

Cor

rela

tions

betw

een

Nat

iona

lEnt

repr

eneu

rial

Fram

ewor

kC

ondi

tions

and

the

Rel

ativ

ePr

eval

ence

ofH

igh-

Expe

ctat

ion

Entr

epre

neur

ialA

ctiv

ity

Familiarity withentrepreneurs

Business angelactivity

TEA (necessity)rate

TEA (opportunity)rate

TEA rate



Baby business 50+

Baby business 20+

Some additional light to the observed patterns can befound in Table 12, which reports correlations for therelative prevalence of high-expectation entrepreneurialactivity. The difference to Table 11 is that in Table 12, theprevalence of high-expectation activity is indicated relativeto the overall rate of entrepreneurial activity. Table 12 thusprovides data for the question: what percentage of allentrepreneurial activity will be characterized as high-expectation activity in different settings? Thus, Table 12looks at the anatomy of entrepreneurial activity, ratherthan the adult-population prevalence rate of high-expectation activity.

Interestingly, the patterns shown in Table 12 are verydifferent from those observed in Table 11. Strong andconsistent positive correlations can be observed for allforms of high-expectation entrepreneurial activity andnational entrepreneurial framework conditions. Again, thecorrelations appear stronger for high-expectation babybusiness activity than for high-expectation nascententrepreneurial activity. Importantly, the patterns are verydifferent than what can be observed for the various TEArates, for which the correlations tend to be either negativeor neutral.

Because the tables report bivariate correlations, oneshould not make too far-reaching conclusions on thebasis of the tables alone. Also, it is advisable not to lookat individual correlations in too much detail, but rather,look at the overall patterns. With these reservations inmind, the patterns in Table 12 may suggest that nationalentrepreneurial framework conditions may have more todo with the anatomy of entrepreneurial activity, ratherthan the overall prevalence of it. The 2004 GEM reportsuggested that the drivers of entrepreneurial activity maybe different in high- and low-income countries. Thehighest overall prevalence rates of entrepreneurial activitycan be observed for low-income countries, such asUganda or Thailand. Earlier in this report, we have seenthat the relative prevalence of high-expectation activityvaries in different world regions, with the lowest relativeprevalence rates displayed for Africa and Latin America.The 2004 GEM report suggested that in low-incomeeconomies, the prevalence of entrepreneurial activity mayhave more to do with the absence of high-quality jobs inthe established corporate sector. This would be consistentwith a relatively higher prevalence rate of low-expectationentrepreneurial activity, as well as necessity-driven activity.As the economy is better able to supply high-quality jobs,the prevalence rate of such activity might drop, and thelocus of forces driving entrepreneurial activity might moveelsewhere, for example, toward innovation. Thus, high-income countries would witness lower overall prevalencerates of entrepreneurial activity but higher relativeprevalence rates of high-expectation activity. While thishypothesis remains to be properly tested, the correlationpatterns reported above are not inconsistent with it.

Finally, it is noteworthy that, in Table 12, the correlations

between relative prevalence of high-expectation activityand the overall prevalence of generic entrepreneurialactivity are negative and neutral, suggesting that, as theoverall prevalence of entrepreneurial activity changes, theanatomy of this activity changes, too, with the relativeprevalence of high-expectation activity increasing as theoverall rate of entrepreneurial activity drops.

ConclusionsHigh-expectation entrepreneurial activity is different fromlow-expectation activity. The analysis above, as well asrecent GEM analyses, suggests that high-expectationentrepreneurial activity may respond to different structuralconditions in the economy than does low-expectationentrepreneurial activity. It may thus represent a distinctfacet of entrepreneurial activity.

Overall, high-expectation entrepreneurial activity tends tobe either positively or neutrally associated with nationalentrepreneurial framework conditions, whereas low-expectation entrepreneurial activity appears negativelyassociated with national entrepreneurial frameworkconditions. Even though no causal inferences can bedrawn from the above analysis, the correlation patternssuggest that different facets of entrepreneurial activitymay be differentially related to national entrepreneurialframework conditions.

The relative prevalence of high-expectation activityappears positively associated with nationalentrepreneurial framework conditions. The analysis abovesuggests that the anatomy of entrepreneurial activity ismore strongly associated with national conditions than isthe overall prevalence of high-expectation activity. Thismay be associated with differential opportunities forentrepreneurship in high- and low-income economies.

The anatomy of entrepreneurial activity appears to changeas the overall level of entrepreneurial activity changes. Inthe data, lower overall levels of entrepreneurial activitywere generally associated with higher relative levels ofhigh-expectation activity. This, too, is suggestive ofdifferential responses of different forms of entrepreneurialactivity to economic conditions.

Active policy has a role to play in promoting high-expectation entrepreneurial activity. Even though directcausal inferences are not possible from the presentanalysis, the evidence of differential relationships withnational conditions for different forms of entrepreneurialactivity suggest that there is room for activeentrepreneurship policy interventions.

It should be possible to design policy interventions thatselectively target high-expectation entrepreneurial activity.If different forms of entrepreneurial activity aredifferentially associated with structural conditions, thenthe design of selective policy measures should bepossible. This is an interesting avenue for furtherresearch.


45


46

The findings reported here confirm the economicimportance of high-potential entrepreneurial activity in jobcreation. Regardless of economic context, high-expectation entrepreneurial ventures promise to create adisproportionate share of new jobs. Studies show that thesame is true for realized job creation data. Our analysisalso shows that the anatomy of the job creationphenomenon varies, with high-income countries generallydepicting lower overall levels of entrepreneurial activity,but higher relative levels of high-expectation activity.Further, our analysis revealed an uneven distribution ofhigh-expectation entrepreneurial activity across industrysectors, selected behavioral data, as well as across basicdemographic variables. These findings suggest importantimplications for government policy.

General Policy ImplicationsOur analysis of national entrepreneurial frameworkconditions and high-expectation entrepreneurial activitysuggests that active policy has a role to play in promotinghigh-expectation and high-growth entrepreneurial activity.This conclusion is also supported by the significantdifferences observed between individual countries, and, inparticular, the high prevalence rates of high-expectationentrepreneurial activity in the Anglo-Saxon countries. Theprevalence rate of high-expectation entrepreneurial activitydoes vary significantly between countries and worldregions, and this prevalence rate appears linked withnational entrepreneurial framework conditions. Therefore,we emphasize the need for an active policy and suggestthree general policy implications from our analysis,applicable in all economic contexts. First, policy-makersshould recognize the importance of high-expectation andhigh-potential entrepreneurial activity and adjust theirpolicy priorities accordingly. Second, policy-makers shouldintroduce an element of selectiveness in entrepreneurshippolicy, to account for the uneven contributions of types ofentrepreneurial activity to job creation. Third, policy-makers should develop sophisticated support measures todeal with the specific support needs of high-expectationentrepreneurial ventures.

It is important for policy-makers to recognize that not allentrepreneurial ventures contribute equally to theeconomy. High-expectation entrepreneurial activityprovides a particularly potent source of new job creation.Awareness of this aspect should be actively promotedwithin policy-making and policy-implementingcommunities so as to enhance the responsiveness ofthese toward high-expectation new ventures. Policy-makers should also promote research to identifyconditions favoring high-expectation activity in their

specific country contexts. Data on country-specific high-expectation activity is important for developing targetedpolicy initiatives. Also, the awareness of the general publicregarding the importance of high-expectationentrepreneurial activity should be enhanced, in order topromote favorable societal attitudes toward high-expectation and high-growth entrepreneurial activity.

The skewed distribution of potential contributionsunderlines the need for selectiveness in policy design andimplementation. If a disproportionate job creation effectis generated by a small sub-set of all entrepreneurialactivity, it is important to develop selective and well-targeted support initiatives, as this is likely to enhance theeffectiveness of support policy28. A policy that provides alittle support for all new ventures is likely to be lesseffective than a policy that attempts to target support athigh-potential ventures. Our analysis suggests that suchtargeting could be possible, for example, by usingbehavioral, demographic, and industry sector criteria.Concerning behavioral criteria, we have observed thathigh-expectation activity appears to be associated withindividual-level informal investor activity, self-efficacy(belief in one’s own ability to successfully start a newventure), familiarity with entrepreneurs, belief in goodopportunities, as well as opportunity motivation. Suchvariables could, potentially, be used in targeting policyinitiatives. Similarly, our analyses show that high-expectation activity is considerably more prevalent insome demographic cells, as opposed to others, withgender, education, income level, and age appearing asparticularly potent influences. Policy measures could bedesigned, for example, to address economic and careerchoice trade-offs within selected demographic cells. Also,addressing high-prevalence demographic cells might, inthe short term at least, yield greater results than moregeneric measures. Finally, our analysis suggests thatsome industry sectors (e.g., manufacturing) see greaterprevalence of high-expectation activity than others. Thus,sector-specific policy initiatives might be designed toaddress high-expectation entrepreneurial activity.

As regards to the sophistication of policy measures,research suggests that high-expectation entrepreneurialactivity has distinctive, and often demanding, supportneeds. In general, providing value-adding support forhigh-growth entrepreneurial ventures tends to be moredemanding than in the case of low-growth entrepreneurialventures. This is because of the high degree oforganizational complexity, as well as the generaldynamism of high-potential and high-growth ventures.Effecting organizational growth, as well as managing it, isdifficult and often also painful. Rapidly growing

Encouraging High-Expectation Entrepreneurship – Implications for Policy

28 Some countries, such as Ireland and Israel, are already implementing highly selective support measures.


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organizations need to manage rapidly increasingcomplexity, as the growing organization needs toconstantly develop new organizational structures and co-ordination mechanisms to cope with new demands. Often,frequent changes in management are necessary, as theskills required for managing different growth stages areseldom possessed by the same management team. Rapidlygrowing ventures are likely to experience greater and morepressing resource needs, concerning both funding andhuman resources. An early internationalization is oftennecessary, particularly if the new venture operates in atechnology-intensive industry sector. Because of theirhighly dynamic character, high-growth new ventures tendto be much more volatile than low-growth ventures, andspectacular successes are therefore likely to beaccompanied by equally spectacular failures.

The special character and needs of high-expectation andhigh-growth new entrepreneurial ventures place particulardemands for support services. It is unlikely that publicsector agencies alone would possess the skills necessaryto provide competent services for high-growth ventures.Rather, policy-makers should attempt to catalyze privatesector provision of such services, either on a stand-alonebasis or through public-private partnerships. Overall, awell-functioning business service infrastructure appearsimportant to cater to the demands of high-growth newventures. Experience in Canada, the USA, and Israelsuggests that classic venture capital is important infostering high-growth, innovative entrepreneurial activity.

Policy-makers should also accept that over-emphasis onsurvival may deduct from experimentation and risk-taking.Predicting growth is difficult, and the true potential of newopportunities can only be tested through experimentation.Therefore, policy-makers should consider ways of reducingthe costs and stigma associated with entrepreneurialfailure, provided that such failure follows an honestattempt to pursue perceived business opportunity.

As regards to the delivery of support services and advice,there is some research to suggest that high-growth newventures value information and experience-based advicereceived from their peers. This emphasizes the need topromote professional links between high-expectationentrepreneurs, perhaps by implementing networkinginitiatives targeted specifically at high-expectation ventures(Fischer and Reuber, 2003).

Conclusions for High-Income CountriesOur analysis suggests that, rather than addressing theoverall level of entrepreneurial activity, policy-makers mightwant to focus on policies that address the anatomy ofentrepreneurial activity. While several high-incomecountries might want to see a greater overall level ofentrepreneurial activity, important progress could also beachieved by focusing specifically on facilitating theoccurrence of high-expectation entrepreneurs. In Europeand in highly developed Asian economies, such policieswould likely entail addressing the economic trade-offsrelated to the entrepreneurial career choice, particularlyamong well-educated and reasonably high-incomedemographic groups (Davidsson & Henrekson, 2002).European and highly developed Asian countries might alsobenefit from a more entrepreneurial culture and of a highersocietal respect for successful high-growth entrepreneurs.

The great majority of high-expectation entrepreneursalready have a job, which underlines the potentialimportance of spin-offs (e.g., from established privatesector companies as well as from public sectorinstitutions) as a mechanism for generating high-expectation entrepreneurial ventures. Policy initiativesdesigned to facilitate spin-off formation, particularly fromknowledge-intensive companies and research institutions,might prove useful in facilitating high-expectationentrepreneurial activity in high-income countries.

In high-income countries that have small domesticmarkets, high-growth entrepreneurial ventures are likely toface the need to internationalize their activities at an earlystage of their life cycles. Internationalization tends toexacerbate the resource needs of high-growth ventures.Therefore, policies designed to facilitate early and rapidinternationalization, particularly in technology-intensivesectors, may prove to be an important mechanism forencouraging high-growth entrepreneurial activity. Thispolicy emphasis may prove useful particularly for manyEuropean economies.

Finally, an important policy priority for high-incomecountries should be to address and, if feasible, remove dis-incentives for entrepreneurial growth. Various studies havesuggested that, e.g., greater compliance requirements as afunction of organizational size, if improperly introduced,may deter some entrepreneurial companies frombypassing a certain threshold size. Similarly, some studieshave suggested that small entrepreneurial ventures have agreater need for flexible employment relationships, as theyhave less slack resources to use as buffer during periods ofweak demand. This may be a particular concern in manyEuropean countries, where it can be difficult and time-consuming to terminate employment relationships.

Conclusions for Low-Income CountriesLow-income countries, in general, do not appear to beexperiencing shortages in general entrepreneurial activity.Indeed, the highest overall prevalence rates ofentrepreneurial activity tend to be observed for low-income countries such as Peru, Uganda, and Thailand. Assuggested by the 2004 GEM Executive Report, thechallenge for low-income countries may therefore not beincreasing the overall level of entrepreneurial activity, butrather, the supply of high-quality jobs, as well asimproving the overall infrastructure for business (Acs,Arenius, Hay, & Minniti, 2004). For low-income countries,therefore, achieving economies of scale may be the firstpriority, and achieving this calls for attention to generalnational framework conditions, instead of frameworkconditions specific to entrepreneurial activity. It is thegeneral framework conditions that are important forachieving economies of scale. As the institutionalframework for any business activity is enhanced, greateremphasis may be given to conditions specific for high-expectation entrepreneurial activity.

Even though low-income countries may have a lowerrelative prevalence rate of high-expectation activity, thisdoes not necessarily mean that the importance of low-expectation and necessity-driven entrepreneurial activityshould be overlooked. In many situations, necessity-driven and low-expectation entrepreneurial activityprovides an important source of income for poorlyeducated individuals. As the overall education level andthe supply of high-quality jobs increase, the rate ofnecessity-driven entrepreneurial activity is likely to fall,and the anatomy of entrepreneurial activity may shifttoward high-expectation activity.


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Endnotes


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