National Entrepreneurial Assessment for the United States of America

Transcription

1 G l o b a l E n t r e p r e n e u r s h i p m o n i t o r National Entrepreneurial Assessment for the United States of America 2009 Executive Report Abdul Ali I. Elaine Allen William D. Bygrave Julio De Castro Julian Lange Thomas S. Lyons Joseph Onochie Ivory Phinisee Edward Rogoff Al Suhu John Whitman

2 Global Entrepreneurship Monitor 2009 National Entrepreneurial Assessment for the United States of America Executive Report Includes an update on Financing Small Business in 2010 Abdul Ali, I. Elaine Allen, William D. Bygrave, Julio De Castro, Julian Lange, Thomas S. Lyons, Joseph Onochie, Ivory Phinisee, Edward Rogoff, Al Suhu and John Whitman 1

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4 Global Entrepreneurship Monitor 2009 National Entrepreneurial Assessment for the United States of America Executive Report Includes an update on Financing Small Business in 2010 The authors thank the Consortium of GEM National Teams who participated in 2009: Algeria, Argentina, Belgium, Bosnia and Herzegovina, Brazil, Chile, China, Colombia, Croatia, Denmark, Dominican Republic, Ecuador, Finland, France, Germany, Greece, Guatemala, Hong Kong, Hungary, Iceland, Iran, Israel, Italy, Jamaica, Japan, Jordan, Latvia, Lebanon, Malaysia, Morocco, Netherlands, Norway, Panama, Peru, Republic of Korea, Romania, Russia, Saudi Arabia, Serbia, Slovenia, South Africa, Spain, Switzerland, Syria, Tonga, Tunisia, Uganda, United Arab Emirates, United Kingdom, United States, Uruguay, Venezuela, West Bank & Gaza Strip, Yemen The authors thank Marcia Cole, Managing Editor, for her numerous contributions to this report. Although GEM data were used in the preparation of this report, their interpretation and use are the sole responsibility of the authors BABSON COLLEGE, BARUCH COLLEGE, Abdul Ali, I. Elaine Allen, William D. Bygrave, Julio De Castro, Julian Lange, Thomas S. Lyons, Joseph Onochie, Ivory Phinisee, Edward Rogoff, Al Suhu and John Whitman 3

5 Table of Contents Introduction 10 The GEM Model 10 GEM and the Link Between Entrepreneurship and Economic Development 14 How GEM Measures Entrepreneurship 15 GEM Website, National Reports and Data Availability 16 GEM Terminology 17 1 Entrepreneurial Behavior in The United States From 2005 to 2009: A Contextual View of the Effects of the Economic Crisis on Entrepreneurial Behavior 18 Trends in Total Entrepreneurship Activity 18 Total Entrepreneurship Activity and Gender 19 Total Entrepreneurship Activity and Age 20 Total Entrepreneurship Activity and Work Status 21 Total Entrepreneurship Activity and Educational Status 22 Total Entrepreneurship Activity and Income 24 Entrepreneurs and Their Motivations 25 Final Comments 27 2 International Comparison: The United States and Other Countries 28 Activity 28 Attitudes 35 Aspirations 39 Conclusion 42 Bibliography 42 3 Does Innovation Drive Entrepreneurship? 43 Innovativeness and Customer Novelty 43 Activity in Technology Sector 45 Spending on Technology 46 Finding Business Success 46 4 Social Entrepreneurship 48 The Key Questions 48 Goals and Values 48 Social, Environmental And Community Objectives 53 5 The Impact of the Recession on the Entrepreneurial Composition of the United States by Region, Population Group and Minorities 55 4

6 Table of Contents 6 Financing 61 Small Business Lending 61 Informal Investing 61 Venture Capital 62 Impact of Economic Declines on Economic Activity 65 7 Public Policy in the United States 65 GEM National Teams Appendix 80 GEM Sponsors 81 Endnotes 85 List of Tables and Exhibits Table 1 Importance of Different Types of National Conditions for Economic Development 12 Table 2 Prevalence Rates (in Percentage) of Entrepreneurial Activity and Business owner-managers Across GEM Countries in 2009, Age Group, by Phase of Economic Development 29 Table 3 United States Survey Demographics, Table 4 Measures of Entrepreneurial Activity 56 Table 5 Measures of Entrepreneurial Activity by White/Caucasian and Non-White/Caucasian 57 Table 6 Entrepreneurial Activity by Gender 58 Table 7 Measures of Entrepreneurship Attitudes Based on Size of Firm 59 Table 8 Industry Distribution by Early-Stage and Established Business Owner Entrepreneurs 60 Table 9 U.S. Percentage Growth Rates by Industry 68 Table 10 Change in U.S. Employment, Business Establishments and Firms 69 Table 11 U.S. Dynamism 71 Table A1 Comparison of Entrepreneurial Activity for 2007, 2008 and 2009 by Phase of Economic Development for the Percentage of the Age Group 80 List of Figures Figure 1 The GEM Model 14 Figure 2 Summarizes the Entrepreneurial Process and GEM s Operational Definitions 16 Figure 3 Total Entrepreneurship Activity, Owner Managed Business and Discontinued Businesses, Figure 4 Percentage of Nascent and New Businesses, Figure 5 Total Entrepreneurship Activity and Gender Figure 6 TEA by Age Group, Figure 7 Percentage of Nascent and New Businesses by Age Group, Figure 8 Work Status Over Time, Figure 9 Percentage of Nascent and New Businesses by Work Status Figure 10 Educational Status Over Time, Figure 11 Percentage of Nascent and New Businesses by Educational Status, Figure 12 Household Income Over Time, Figure 13 Percentage of Nascent and New Businesses by Household Income

7 Table of Contents Figure 14 Entrepreneur Knows an Entrepreneur 26 Figure 15 Entrepreneur Sees Good Opportunities for Starting a Business 26 Figure 16 Entrepreneur Has Knowledge and Skills 26 Figure 17 Sector Distribution Early-Stage Entrepreneurial Activity (18 64 Age Group), Figure 18 Sector Distribution Established Business (18 64 Age Group), Figure 19 Early-Stage Entrepreneurial Activity for Separate Age Groups, Figure 20 Total Early-Stage Entrepreneurial Activity (TEA) Rates, , Averages 32 Figure 21 Nascent Entrepreneurial Activity Rates, , Averages 33 Figure 22 Necessity-Driven Opportunity TEA Rates, , Averages Over Efficiency-Driven Countries, Innovation-Driven Countries and the United States 34 Figure 23 Total Early-Stage Entrepreneurship by Gender 35 Figure 24 Perceived Opportunities for Starting a Business, Figure 25 Fear of Failure Among Those Who Perceive Good Start-Up Opportunities, Figure 26 Perceived Skills and Knowledge to Start a New Business, Figure 27 Intentions to Start a New Business in the Next Three Years, Figure 28 High-Growth Expectation Early-Stage Entrepreneurial Activity by Country (18 64 Age Group) 40 Figure 29 Figure 30 Figure Entrepreneur s Views on Starting a Business in Comparison to One Year Ago by Phase of Economic Development (Unweighted Country Averages) %Yes Entrepreneur s Views on Growing a Business in Comparison to One Year Ago by Phase of Economic Development (Unweighted Country Averages) %Yes 41 Impact of the Global Economic Slowdown on Entrepreneurs Perception of Opportunities for their Business, According to the Entrepreneurs (Unweighted Country Averages) %Yes 42 Figure 32 Percentage of Business Entities with New Product-Market Combinations 43 Figure 33 Impact of Business Entities Perceptions of Economic Outlook on Innovation 44 Figure 34 Percentage of Business Entities Active in Medium- or High-Technology Sector 45 Figure 35 Percentage of Business Entities Spending on Technology in Figure 36 Percentage of Business Entities Finding Success 47 Figure 37 Entrepreneurship Goals Among Start-Ups 49 Figure 38 Entrepreneurship Goals Among Ongoing Ventures 50 Figure 39 Goals by Gender for Start-Ups 51 Figure 40 Goals by Gender for Ongoing Businesses 52 Figure 41 Venture Capital IRR and Total Amount Raised by VC-Backed IPOs 63 Figure 42 Venture Capital Investments in Clean-Tech in United States 64 Figure 43A U.S. Entrepreneurial Trends with Real GDP 66 Figure 43B Percent Changes: U.S. Real GDP and Key Components 67 Figure 44 GEM U.S. National Expert Survey Mean Response for New Firm Entrepreneurship Opportunity 70 Figure 45 GEM U.S. National Expert Survey Mean Response for Available Funding 71 Exhibits Exhibit 1 Early-Stage Entrepreneurship Among Ethnicities 56 6

8 Executive Summary The United States Has Stability in Established Businesses, but Stalls on Other Fronts The Global Entrepreneurship Monitor (GEM) data is used to present an analysis of entrepreneurial behavior trends for the years 2005 to One of the more startling findings is that the amount of total early-stage entrepreneurial activity (TEA) in the United States has been in an almost constant decline for the past five years, dropping from 10.6% in 2005 to 6.9% in 2009 (using the broader age group), and characteristics of entrepreneurial behavior in The results for 2009 are consistent with the prior trend. When using the age group, the traditional age range used for GEM global analysis, entrepreneurial activity in the United States dropped from 10.8% in 2008 to 7.9% in This is a breathtaking 27% drop. The GEM data clearly indicate that fewer businesses are being started now than in the past. However, there is evidence that the businesses that do survive the crisis to reach 42 months old are more likely to stay in business. The indication that businesses that start-up now might be more sustainable could be explained by the role that financing plays on business survival. Perhaps the businesses starting up now are better capitalized. The examination of entrepreneurial activity specifically in 2009 is important because of the economic turmoil suffered by the United States and the world during the past two years. An important question is what has been the effect of entrepreneurship on new venture creation during this period. Our analysis confirms the stifling effect the recession has had on new venture creation. Though the effects of the recession were felt nationwide, GEM data shows a disproportionate impact on certain regions such as the Midwest. In the Midwest, the percentage of total early-stage entrepreneurship dropped significantly from 7.4% to 4.8%, and the owner managers of businesses less than 42 months old fell from 3.2% to 1.4%. The patterns in the Midwest are to a lesser extent reflected in other regions, as well as the country as a whole. In the Northeast, it is worth noting that early-stage entrepreneurial activity declined from 8.0% in 2008 to 6.4% in 2009 and owner managers of businesses less than 42 months old dropped from 3.7% to 1.8%; meanwhile, necessity-driven entrepreneurship was on the rise. Across the United States, this represents a clear picture of declining numbers of new, highgrowth potential ventures and an increase in smaller necessity-driven ventures, which, data has shown, are more likely to fail. Just as particular regions have suffered more from the recession than others, GEM data draw a parallel with specific populations as well. Non-Caucasian samples are small, particularly when broken out by region, so one must be careful about over-interpreting results. However, the results are convincing that early-stage entrepreneurial activity among non-caucasians in the Midwest has dropped considerably, by perhaps as much as half. The finding that non-caucasian entrepreneurs have suffered significantly in this recession does not bode well for the U.S. economy, which needs entrepreneurial activity at all levels and from all directions to stimulate growth and development. The one bright spot in this otherwise dismal scenario is a drop in the percentage of non-caucasian entrepreneurs who closed their businesses in 2009 as compared to Nationwide, 2.9% reported shuttering their establishments in 2008, while only 1.4% reported closing their ventures in Even in the Midwest there was a decrease in reported business closures among non-caucasians. Downturns in regional economies and among ethnic communities will produce widespread financial strains and increase demands for business support services that could exceed government and private organization capacities. Moreover, population and demographic patterns among entrepreneurs could be permanently reshaped by the recession. The report goes on to examine the characteristics of entrepreneurial behavior and the prevalence rates for four types of entrepreneurs: 1) early stage, 2) established, 3) opportunity-driven and 4) necessity-driven. The results indicate a pattern of decline for early stage, established and opportunitydriven entrepreneurs, especially between 2008 and Moreover, the number of established entrepreneurs those in business for 42 months or longer, declined over the same period as did the number of opportunity-driven entrepreneurs. Only necessity-driven entrepreneurs increased in number consistent with what is known about behavior during an economic crisis: As the economy sheds jobs, more individuals start businesses to sustain themselves. Contrary to past performance, when the United States led innovation-driven economies in early-stage opportunity-based entrepreneurship, in 2009 the United States lost its foothold. Today, opportunitybased entrepreneurship represents about the same percentage of total early-stage activity (%TEA) in the United States as it does in the average innovationdriven economies. Likewise, the United States with a history of particularly low levels of necessity-driven entrepreneurship compared to other GEM countries 7

9 Executive Summary 8 now finds a reversal of fortune. Whereas the vast majority of individuals starting businesses in the United States traditionally reported that they did so because of an opportunity in the market, that is not the case in It is important for GEM to continue to track this development in order to assess whether this is a one-year blip or the beginning of a new and worrisome trend. The United States has always been an innovationdriven economy, which makes it important to understand and support the innovative activity of its entrepreneurs in order to maintain an economic edge in the world. In fact, it is imperative to know how U.S. entrepreneurs are coping with the need for innovation in the aftermath of the financial crisis of Therefore, GEM asked specific questions in the United States 2009 survey to ascertain information about the innovativeness of products and services, entrepreneurial involvement in the technology sector, use of new technology and intended expenditures on new technology. Data show that product offerings were less innovative in 2009 compared to the previous year. The decline in product innovativeness, however, was most pronounced for established business owners (25.7% of entrepreneurs reported developing innovative products in 2009; the corresponding number for established businesses was 9.9%). Developing fewer novel products or services by U.S. entrepreneurs may signal the worsening economic situation in Interestingly, it seems that the recession and economic turmoil positively affected the perceptions of entrepreneurs about their businesses. Entrepreneurs and established business owners views of the 2009 economic downturn influenced their commitment to developing innovative products. Although sharing the same grim economic outlook, on the whole, a larger proportion of early-stage entrepreneurs are developing new product-market combinations than are established business owner-managers (7 to 11.7% of the entrepreneurs compared to 4.7 to 7.4% of the established business owner-managers. Such positive commitment to innovation may serve these entrepreneurs well once the economic crisis is over. Examination of entrepreneurial activities by industries shows extractive and transforming industries changed the least between 2008 and 2009, while business services companies show the largest declines. Interestingly, entrepreneurial activity in the consumer-oriented sector actually increased for both early-stage businesses jumping from 35.9% to 41.1% and established businesses, which went from 32.8% to 34.3%. One can only speculate as to the reasons that this would happen in a declining economy where consumer spending has dropped by historic amounts. Perhaps the low capital and technical training requirements of these businesses relative to other sectors allowed these entrepreneurs to both start and continue their businesses during a recession. Additionally, it may be that other barriers to entry in the business services group are perceived as being lower. However, one must be quite skeptical about the survival prospects of these companies if consumer spending does not rebound. With regards to social entrepreneurship, the GEM 2008 data indicated increasing interest in social entrepreneurship as an approach to business startup and growth, and in 2009 social entrepreneurship continued to capture the imagination of the world. Yet the percentage of ongoing ventures motivated by economic goals remains higher than ventures with other goals, which may indicate that ventures focusing on financial performance rather than social mission are better able to endure the downturn. GEM data also allows for comparison between the entrepreneurial behavior in the United States and that of other countries. Of innovation-driven economies, the United States is among those countries with the highest prevalence rates for nascent entrepreneurial activity and is higher than the average when measuring TEA. However, when measuring established business prevalence rates, the United States compares less favorably,falling below the average in all three economies (innovation-, factorand efficiency-driven). The GEM data show that, in 2009, starting a business was more difficult in general than growing an existing business. The U.S. business discontinuation rate was slightly higher than the average for the innovationdriven countries in Finally, we examined the financing of new ventures; in particular, the troubles of 2009 affected the financing of new ventures. Every new venture, from mom-and-pop convenience stores to Silicon Valley superstars such as Google, starts with an investment from the founders themselves or the so-called 3Fs (Family, Friend or Foolhardy strangers). Those informal investors are vital to the start-up process; if all of them stopped providing money to start-ups, the U.S. economy would immediately feel the effect with a sudden jump in unemployment. What s more, informal investments flow almost instantaneously into the economy when entrepreneurs spend their investments to buy goods and services for their new ventures; thereby informal investment supports many more secondary jobs (the multiplier effect). Because informal investment is relatively rare in most countries, the GEM measure of informal investment asks respondents if they have invested in someone else s new business in the past three years. It is therefore a smoothed measure; it is not a measure

10 Executive Summary of activity in just one year. Thus it is not possible to give precise numbers for the amount of informal investment in a given year, but it is possible to infer year-to-year changes and their approximate magnitude. As it turned out, in 2009, the United States informal investor prevalence rate was the third lowest since 1999, when GEM began its annual surveys. Two factors, the recession and the aftermath of the bursting of the global financial crisis, combined with other factors to produce the weakest market for initial public offerings (IPOs) of venture-backed companies in both 2008 and 2009 since at least When the IPO market is booming, the returns on venture capital are high, and vice versa. The one-year returns for 2008 were awful because the IPO market was dismal, with only 6 IPOs in the entire 12 months compared with 86 in Indeed, the IPO market was so dreadful in 2008 and the first half of 2009 that no venture capital-backed companies went public in the second quarter of 2008 or the first quarter of 2009; the last time that happened was more than 30 years ago! Clean technology is attracting a lot of venture capital, with the amount invested increasing ten-fold from $0.4 billion in 2004 to $4.1 billion in 2008; however, investment in clean technology fell in 2009 along with venture capital investment in general. 9

11 Introduction The year 2009 will be remembered for an economic recession that shattered the economic landscape in most countries across the world. As national and regional governments search for ways of rebuilding their economies, our understanding of the relationship between entrepreneurship and development remains incomplete. Partly, this is because research in these two fields has tended to run along separate paths. Progress has also been hampered by a lack of crossnational harmonized data sets on entrepreneurship. Since 1997, GEM has sought to address these gaps by collecting relevant harmonized data on an annual basis and by bringing academic experts in entrepreneurship from across the globe to work together on a common research program. GEM focuses on three main objectives: To measure differences in the level of entrepreneurial activity among countries To uncover factors determining national levels of entrepreneurial activity To identify policies that may enhance the national level of entrepreneurial activity Traditional analyses of economic growth and competitiveness have tended to neglect the role played by new and small firms in national economies. GEM takes a comprehensive approach and considers the degree of involvement in entrepreneurial activity within a country, identifying different types and phases of entrepreneurship. While the first GEM reports included high-income countries only, the ambition has always been to include as many countries as possible in order to aid policy makers in their efforts to stimulate economic development through entrepreneurial activity. In 2009, the number of countries participating in GEM rose by over 25% to 54 countries. These countries vary greatly in terms of economic development. As an aid to presentation, we categorize them into three groups: factor-driven economies, which are primarily extractive in nature;efficiency-driven economies in which scale-intensity is a major driver of development; and innovation-driven economies. i The rest of this chapter is devoted to an explanation of the methodology behind GEM. Chapter 2 details three dynamic interactive components of entrepreneurship: entrepreneurial attitudes, activity and aspiration, using the results of the GEM Adult Population Surveys in each participating nation. Chapter 3 focuses on the impact of the global crisis on entrepreneurship and discusses the role entrepreneurship plays in getting out of recessions. Each year, the GEM report highlights one aspect of the GEM conceptual model. Chapter 4 provides the first ever standardized estimates of social entrepreneurship across the globe. Extra questions on this special topic were included in the GEM Adult Population Survey (APS) and the standard National Expert Survey (NES) this year. Finally, Chapter 5 contrasts the role and extent of informal investment and venture capital in the countries covered by GEM in The Gem Model There is wide agreement on the importance of entrepreneurship for economic development. ii Business entrepreneurs drive and shape innovation, they speed up structural changes in the economy and they introduce new competition, thereby contributing to productivity. Social entrepreneurs perform a similar function in the social economy, filling gaps in social needs that are left unfilled or poorly addressed by both business and governments. While important, the contribution of entrepreneurs to an economy also varies according to its phase of economic development. iii This report is framed around a model, introduced in the GEM 2008 report, that includes a distinction among phases of economic development, in line with Porter s typology of factordriven economies, efficiency-driven economies and innovation-driven economies (Porter, Sachs and McArthur, 2002). As previous GEM reports have shown, necessity-driven self-employment activity tends to be higher in less developed economies. Such economies are unable to keep pace with the demand for jobs in high-productivity sectors, and so many people must create their own economic activity. As an economy develops, the level of necessity-driven entrepreneurial activity gradually declines as productive sectors grow and supply more employment opportunities. At the same time, opportunitydriven entrepreneurial activity tends to pick up with improvements in wealth and infrastructure, introducing a qualitative change in overall entrepreneurial activity. Further details on the role of entrepreneurship in different phases of economic development are provided in Box 1. 10

12 Introduction Box 1 The Role of Entrepreneurship in Different Phases of Economic Development Entrepreneurship in Factor-Driven Economies Economic development consists of changes in the quantity and character of economic value added (Lewis, 1954). These changes result in greater productivity and rising per-capita incomes, and they often coincide with migration of labor across different economic sectors in the society, for example, from primary and extractive sectors to the manufacturing sector, and eventually services (Gries & Naude, 2008). Countries with low levels of economic development typically have a large agricultural sector, which provides subsistence for the majority of the population who mostly still live in the countryside. This situation changes as industrial activity starts to develop, often around the extraction of natural resources. As extractive industry starts to develop, this triggers economic growth, prompting surplus population from agriculture to migrate toward extractive and emergent scale-intensive sectors, which are often located in specific regions. The resulting oversupply of labor feeds subsistence entrepreneurship in regional agglomerations, as surplus workers seek to create self-employment opportunities in order to make a living. Entrepreneurship in Efficiency-Driven Economies As the industrial sector develops further, institutions start to emerge to support further industrialization and the build-up of scale in the pursuit of higher productivity through economies of scale. Typically, national economic policies in scaleintensive economies shape their emerging economic and financial institutions to favor large national businesses. As increasing economic productivity contributes to financial capital formation, niches may open in industrial supply chains that service these national incumbents. This, combined with the opening up of independent supply of financial capital from the emerging banking sector, would spur opportunities for the development of smallscale and medium-sized manufacturing sectors. Thus, in a scale-intensive economy, one would expect necessity-driven industrial activity to gradually fall and give way to an emerging smallscale manufacturing sector. Entrepreneurship in Innovation-Driven Economies As an economy matures and its wealth increases, one may expect the emphasis in industrial activity to gradually shift toward an expanding service sector that caters to the needs of an increasingly affluent population and supplies the services normally expected of a high-income society. The industrial sector evolves and experiences improvements in variety and sophistication. Such a development would be typically associated with increasing research & development and knowledge intensity, as knowledge-generating institutions in the economy gain momentum. This development opens the way for the development of innovative, opportunity-seeking entrepreneurial activity that is not afraid to challenge established incumbents in the economy. Often, small and innovative entrepreneurial firms enjoy an innovation productivity advantage over large incumbents, enabling them to operate as agents of creative destruction. To the extent that the economic and financial institutions created during the scale-intensive phase of the economy are able to accommodate and support opportunityseeking entrepreneurial activity, innovative entrepreneurial firms may emerge as significant drivers of economic growth and wealth creation. KEY AREAS OF INTEREST FOR POLICY MAKERS Since entrepreneurial activities vary with economic development, national policy makers need to tailor their socio-economic programs to the development context of their country. Table 1 provides a simple guide to likely priorities for each major phase of economic development. iv Whereas enabling entrepreneurship in factor-driven economies may be desirable, more basic requirements such as primary education are necessary and should have priority, as entrepreneurship is unlikely to contribute substantial improvements in wealth creation if basic requirements are in bad shape. Entrepreneurs with high aspirations fare better in countries with a stable economic and political climate and well-developed institutions (in fact they may migrate to other countries to pursue their ideas). In other words, entrepreneurship should certainly not be discouraged, but improving the entrepreneurial framework conditions should perhaps not attract too many financial resources in this phase of economic development if it is at the expense of basic requirements. The Doing Business Project of the World Bank has shown how relatively low cost interventions can dramatically lower the cost of entry to the formal economy. This is a good example of what poorer countries can do to enhance 11

13 Introduction their entrepreneurial economy without making major sacrifices in important basic programs. At the other end of the spectrum, policy makers in some of the most advanced countries would do well to enhance entrepreneurial framework conditions, as this should make their economy more dynamic and innovation-oriented. However, this assumes that they have high quality basic requirements and efficiencyenhancing conditions in place. In some developed countries, there is increasing concern over the effect of deteriorating transportation infrastructure on the economy, while in others, projected shortages of power could do more to impede entrepreneurial activity than policies aimed at promoting entrepreneurship could ever do to enhance it. Table 1 Importance of Different Types of National Conditions for Economic Development Basic Requirements Efficiency Enhancers Entrepreneurial Conditions Factor-Driven Economies Key Focus Develop Start Enabling Efficiency-Driven Economies Maintain Key Focus Develop Innovation-Driven Economies Maintain Maintain Key Focus 12 ENTREPRENEURSHIP: ATTITUDES, ACTIVITY AND ASPIRATIONS Different opinions on, and therefore different definitions of, entrepreneurship can be observed in the academic literature, in policy documents and in the media. The GEM model accepts the multifaceted nature of entrepreneurship. It recognizes that a range of environmental conditions affect three main components of entrepreneurship: attitudes, activity and aspirations, and that this dynamic mix produces new economic and socially valuable activity, generating jobs and wealth. Entrepreneurial attitudes are attitudes toward entrepreneurship. For example, the extent to which people think there are good opportunities for starting a business, or the degree to which they attach high status to entrepreneurs, might be termed entrepreneurial attitudes. Other relevant attitudes might include the level of risk that individuals might be willing to bear and individuals perception of their own skills, knowledge and experience in business creation. Entrepreneurial attitudes can influence entrepreneurial activity but can also be influenced by entrepreneurial activity. For example, the legitimacy of entrepreneurship in a society, as expressed in positive entrepreneurial attitudes, can be influenced by whether people know anyone who has started a business recently. This can be a function of both levels of entrepreneurial activity and social networking activity in the society. Individuals who know other individuals who recently started a business may, through familiarity with the process, be more likely to see it as legitimate. Entrepreneurial attitudes are important because they express the general feelings of the population toward entrepreneurs and entrepreneurship. Countries need people who can recognize valuable business opportunities and who perceive they have the required skills to exploit these opportunities. Moreover, if national attitudes toward entrepreneurship are positive, this will generate cultural support, help, financial resources and networking benefits to those who are already entrepreneurs or want to start a business. Entrepreneurial activity can take on many forms, but one important aspect is the extent to which people in a population are creating new business activity, both in absolute terms and relative to other economic activities, such as business closure. Within the realm of new business activity, different types of entrepreneurial activity can be distinguished. For example, business creation may vary by industry sector, by the size of the founding team, and by whether the new venture is legally independent of other businesses, and in terms of founder demographics, such as gender, age or education. Entrepreneurial activity is best seen as a process rather than an event. v That is why GEM measures entrepreneurial intentions, nascent, new and established business activity and business discontinuation activity. Section 1.2 explains how these concepts are measured in GEM. Examining multiple components of entrepreneurial activity also allows us to explore differences among the entrepreneurial processes across the three major phases of national economic development. For example, new business activity is expected to be high in factor-driven economies mainly because much of it is motivated by economic necessity. In innovationdriven economies, the proportion of opportunitydriven entrepreneurship is expected to be higher than in factor- and efficiency-driven economies.

14 Introduction Entrepreneurial aspiration reflects the qualitative nature of entrepreneurial activity. For example, entrepreneurs differ in their aspirations to introduce new products and new production processes, to engage with foreign markets, to develop a significant organization and to fund growth with external capital. These aspirations, if they are realized, can significantly affect the economic impact of these entrepreneurial activities. Product and process innovation, internationalization and ambition for high growth are regarded as hallmarks of ambitious or high aspiration entrepreneurship. GEM has created measures that capture such aspirations. ENTREPRENEURIAL FRAMEWORK CONDITIONS Entrepreneurial Framework Conditions (EFCs) reflect major features of a country s socio-economic milieu that are expected to have a significant impact on the entrepreneurial sector. The GEM model maintains that, at the national level, different framework conditions apply to established business activity and to new business activity. The relevant national conditions for factor-driven economic activity and efficiency-driven economic activity are adopted from the Global Competitiveness Report (GCR) (Schwab 2009). With respect to innovationdriven economic activity, the GEM model contributes to the GCR perspective on economic development by identifying framework conditions that are specific to innovation and entrepreneurship (see Levie and Autio 2008 for a theoretical underpinning). As Acs and Armington (2006), among others, propose, it is the entrepreneurial mechanism that turns innovation into economic output. A lack of entrepreneurship can therefore be seen as a bottleneck for innovationdriven countries in achieving their growth ambitions. It is important to recognize that all three principal types of economic activity: factor-driven, efficiencydriven and innovation-driven, are present in all national economies. But their relative prevalence and their contribution to economic development varies. The GCR proposition is that each phase of economic development has a different optimal combination of these three activities. The three phases are labeled according to the activity that is most significant for that phase. Thus, the relative importance of entrepreneurial framework conditions to a country s advancement in economic development may vary by phase of economic development. The GEM model is presented in Figure 1. For factor-driven economies, emphasis is put on basic requirements: development of institutions, infrastructure, macroeconomic stability, health and primary education. These basic requirements are necessary, and may be sufficient, to sustain necessitybased entrepreneurship, but may be insufficient to nurture sophisticated forms of opportunitybased entrepreneurship. It is important to realize that the model does not suggest that necessitybased entrepreneurship should be discouraged. For example, in countries with a stable political environment, necessity-based entrepreneurs who can make a living for their families could also support their children s education. This could give them a better position on the job market or better qualifications to become opportunity-based entrepreneurs. As economies progress and scale economies become more and more relevant, other conditions, which are called efficiency enhancers, ensure a proper functioning of the market, and becoming an employee may become more economically attractive from an individual perspective than necessity-based entrepreneurship, as well as more efficient from a national perspective. Even though these conditions are not directly related to entrepreneurship in the Schumpeterian sense, they are indirectly related since the development of markets will also attract more opportunity-based entrepreneurship. For wealthy countries with high labor costs whose economic development is primarily innovation-driven, entrepreneurial framework conditions become more important as levers of economic development than basic requirements or efficiency enhancers. To summarize: Entrepreneurship is a key mechanism for economic development in every phase. The impact of entrepreneurship on development is likely to differ in each phase in terms of time lag and size. The relative emphasis of policy makers on basic requirements, efficiency enhancers, innovation and entrepreneurship is key to development in each phase: a. For factor-driven economies, getting the basic requirements right is key to the generation of sustainable businesses that can contribute not just to local economic activity but to health and education of the next generation. b. For efficiency-driven countries, the nurturing of economies of scale attracts more growth- and technology-oriented entrepreneurs, creating more employment opportunities. c. For innovation-driven countries, the focus lies more on dynamics and stimulating new combinations of products and markets. 13

15 Introduction Figure 1 The GEM Model Social, Cultural, Political Context From Other Available Sources From GEM National Expert Surveys (NES) Basic Requirements - Institutions - Infrastructure - Macroeconomic Stability - Health and Primary Education Efficiency Enhancers - Higher education and Training - Goods Market Efficiency - Labor Market Efficiency - Financial Market Sophistication - Technological Readiness - Market Size Innovation and Entrepreneurship - Entrepreneurial Finance - Government Policies - Government Entrepreneurship Programs - Entrepreneurship Education - R&D Transfer - Commercial, Legal Infrastructure for Entrepreneurship - Internal Market Openness - Physical Infrastructure for Entrepreneurship - Cultural, Social Norms Established Firms (Primary Economy) Activity: Early-Stage Persistence Exits Entrepreneurship Attitudes: Perceived Opportunities Perceived Capacity Aspirations: Growth Innovation Social Value Creation From GEM Adult Population Surveys (APS) New Branches, Firm Growth National Economic Growth (Jobs and Technical Innovation) 14 GEM AND THE LINK BETWEEN ENTREPRENEURSHIP AND ECONOMIC DEVELOPMENT The GEM model set out above documents how entrepreneurship is affected by national conditions. It also shows that GEM considers three major components of entrepreneurship: attitudes, activity and aspirations. GEM monitors entrepreneurial framework conditions in each country through harmonized surveys of experts in the field of entrepreneurship (see section 2.4). Components of entrepreneurship are tracked using the adult population surveys. Thus GEM generates original data on the institutional framework for entrepreneurship and entrepreneurial attitudes, activity and aspirations using its own methodology, which is harmonized across countries. In this report, we do not attempt to estimate the effect of entrepreneurship on economic development using GEM data. This relationship is a rather complex one. Different types and phases of entrepreneurship may impact economic growth differently in different parts of the world (Sternberg and Wennekers, 2005). In addition, in theory the relationship works both ways: Entrepreneurship may impact economic development, which in turn may impact entrepreneurship. Disentangling these reinforcing relationships requires a careful time series analysis. This is something that will be possible using the rich GEM dataset in the near future (see Chapter 3) and some initial findings can be found in Van Stel et al. (2005), Acs and Varga (2005), Acs and Amoros (2008) and Koellinger and Thurik (2009). Based on the existing evidence on the link between entrepreneurship and economic growth, and projecting this evidence onto the GEM data, GEM researchers Zoltan Acs and Laszlo Szerb (2009) developed a Global Entrepreneurship Index (GEI). Two main assumptions served as their point of departure: (i) attitudes, activity and aspirations need to be included in such an index; (ii) the effect of these components on economic development is a function of the presence and level of specific institutional conditions.

16 Acs and Szerb identify several components for each sub-indicator. Typically, these components consist of one genuine entrepreneurship indicator (mostly derived from GEM data) and one institutional climate indicator (mostly from sources outside GEM). As an example, Acs and Szerb argue that opportunitydriven entrepreneurial activity makes a bigger contribution to economic development when doing business has been made easier in the country. Thus, they combine the GEM measure of opportunity-driven early-stage entrepreneurial activity with the World Bank s measure of ease of doing business into one measure. vi This measure is in turn combined with five other measures dealing with entrepreneurial activity, forming a sub-indicator of entrepreneurial activity. Finally, combining three sub-indicators dealing with entrepreneurial attitudes, activity and aspiration results in an overall index for entrepreneurship: the Global Entrepreneurship Index (GEI). For national and regional policy makers, it is possible to track which components score relatively poorly and which components appear relatively healthy. To this end, a policy tool has been developed that provides an at a glance picture of the state of entrepreneurship in a country or region. In conclusion, the Global Entrepreneurship Index and the GEM model are compatible in that they follow the same model. But whereas the monitor (GEM) focuses on giving the results based on primary data collection, the index (GEI) uses these results, assumes certain links with institutions and economic development and combines the measures to form an index. Further information on the GEI is available from the GEM website. HOW GEM MEASURES ENTREPRENEURSHIP The previous section showed that entrepreneurship is a complex phenomenon that spans a variety of contexts. In line with its objectives, GEM takes a broad view of entrepreneurship and focuses on the role played by individuals in the entrepreneurial process. Unlike most entrepreneurship data sets that measure newer and smaller firms, GEM studies the behavior of individuals with respect to starting and managing a business. This differentiates GEM data from other data sets, most of which record firm-level data on (new) firm registrations, as highlighted in the 2008 GEM global report (see Bosma et al., 2009, p. 12). New firms are, most often, started by individuals. Even in established organizations, entrepreneurial attitudes, activities and aspirations vary by the individual. Another guiding principle of GEM Research is that entrepreneurship is a process. Therefore, GEM observes the actions of entrepreneurs who are at different stages of the process of creating and sustaining a business. For GEM, the payment of any wages for more than three months to anybody, including the owners, is considered to be the birth event of actual businesses. Individuals who are actively committing resources to start a business that they expect to own themselves but who have not reached this birth event are labeled nascent entrepreneurs. Individuals who currently own and manage a new business that has paid salaries for more than 3 months but not more than 42 months are known as new business owners. The cut-off point of 42 months has been made on a combination of theoretical and operational grounds. vii The prevalence rate of nascent entrepreneurs and new business owners taken together may be viewed as an indicator of early-stage entrepreneurial activity in a country. It represents dynamic new firm activity the extent of experimentation in new business models by a national population. Established business owners own and manage a business that has been in operation for more than 42 months. Their businesses have survived the liability of newness. High rates of established business ownership may indicate positive conditions for firm survival. However, this is not necessarily the case. If a country exhibits a high degree of established entrepreneurship combined with a low degree of early-stage entrepreneurial activity, this indicates a low level of dynamism in entrepreneurial activity. Finally, GEM identifies individuals who have discontinued a business in the last 12 months. These individuals may enter the entrepreneurial process again. The GEM 2009 Global Executive Report includes 54 countries across the globe. In each of these 54 countries, a survey was held among a representative sample of at least 2,000 adults. More than 180,000 adults were interviewed between May and October (outside holiday seasons) and answered questions on their attitudes toward and involvement in entrepreneurial activity. viii Appendix 2 contains specific definitions of measures of entrepreneurial attitudes, activity and aspirations used in this report. Care should be taken in comparing data provided in this report with previous reports, as definitions of some measures may have changed. 15

17 Figure 2 Summarizes the Entrepreneurial Process and GEM s Operational Definitions Discontinuation of Business Total Early-Stage Entrepreneurial Activity (TEA) Potential Entrepreneur: Opportunities, Knowledge and Skills Nascent Entrepreneur: Involved in Setting Up a Business Owner-Manager of a New Business (up to 3.5 years old) Owner-Manager of an Established Business (more than 3.5 years old) Conception Firm Birth Persistence GEM WEBSITE, NATIONAL REPORTS AND DATA AVAILABILITY GEM is a consortium of national teams participating in the Global Entrepreneurship Research Association (GERA the umbrella organization that hosts the GEM project). Thanks to the effort and dedication of hundreds of entrepreneurship scholars as well as policy advisors across the globe, the GEM consortium is a unique network building a distinct data set. Contact details, GEM 2009 National Summary Sheets, and national teams micro-sites can be found on The GEM national reports, produced by the national teams, provide more in-depth information on the specific countries. A selection of GEM data is also made available on this Website and tables can be downloaded free of charge using drop-down menus. The GEM Website also provides an updated list of the growing number of peer-reviewed scientific articles based on GEM data. 16

18 GEM Terminology Nascent Entrepreneur New Firm Entrepreneur Established Business Owner New Business Ownership Rate Dynamism Total Early-Stage Entrepreneurial Activity (TEA Rate) Overall Entrepreneurial Activity Rate Business Discontinuation Rate Fear-of-Failure Rate Entrepreneurial Intention Perceived Opportunities Perceived Capabilities Necessity-Driven Entrepreneurial Activity Improvement-Driven Opportunity Entrepreneurial Activity A nascent entrepreneur is one who is actively planning a new venture. Such an entrepreneur has done something during the previous 12 months to help start a new business that he or she will own, at least in part. Activities such as organizing the start-up team, looking for equipment, saving money for the start-up or writing a business plan would all be considered active commitments to starting a business. Wages or salaries will have been paid for no more than three months; nascent entrepreneurs are often still employed full-time elsewhere. A new firm entrepreneur is an entrepreneur who, at least in part, owns and manages a new business that is between 4 and 42 months old and has not paid salaries for longer than this period. In addition to those individuals who are currently involved in the early stages of a business, there are also many individuals who have set up businesses that they have continued to own and manage for a longer time. These individuals are included in the established business owner index, which captures the percentage of individuals in a population who have set up businesses that they continue to own and manage and who have paid wages or salaries for more than 42 months. Percentage of the age group who are currently owner-managers of new businesses, i.e., owning and managing a running business that has paid salaries, wages or any other payments to the owners for more than 3 months but not more than 42 months. As used in this report, dynamism is defined as the ratio of early-stage entrepreneurship to established business ownership. This ratio shows the relative activity levels among early-stage entrepreneurs compared to the prevalence of established business owners. Low levels of dynamism indicate a less entrepreneurial environment. As its name implies, total early-stage entrepreneurial activity refers to the total rate of early-stage entrepreneurial activity among adults in the age group, inclusive. In some instances, this rate is less than the combined percentages for nascent and new firm entrepreneurs because in circumstances where a respondent qualifies as both a nascent and a new firm entrepreneur, he or she is counted only once. Percentage of the age group who are currently engaged in early-stage entrepreneurial activity or owner-manager of an established business (as defined above). Percentage of the age group who have, in the past 12 months, discontinued a business, either by selling, shutting down or otherwise discontinuing an owner-management relationship with the business. Note: This is not a measure of business failure rates. Percentage of the age group with positive perceived opportunities (individuals involved in any stage of entrepreneurial activity excluded) who indicate that fear of failure would prevent them from setting up a business. Percentage of the age group (individuals involved in any stage of entrepreneurial activity excluded) who intend to start a business within three years. Percentage of the age group (individuals involved in any stage of entrepreneurial activity excluded) who see good opportunities to start a firm in the area where they live. Percentage of the age group (individuals involved in any stage of entrepreneurial activity excluded) who believe they have the required skills and knowledge to start a business. Percentage of those involved in early-stage entrepreneurial activity (as defined above) who are involved in entrepreneurship because they had no other option for work. Percentage of those involved in early-stage entrepreneurial activity (as defined above) who (i) claim to be driven by opportunity as opposed to finding no other option for work; and (ii) indicate the main driver for being involved in this opportunity is being independent or increasing their income, rather than just maintaining their income. 17

19 1 Entrepreneurial Behavior in the United States from 2005 to 2009: A Contextual View of the Effects of the Economic Crisis on Entrepreneurial Behavior Julio De Castro and I. Elaine Allen The Global Entrepreneurship Monitor s 2009 United States Report puts emphasis on the examination of the effects of the Recession on entrepreneurial behavior in the United States. However, for an appropriate assessment of the phenomenon, it is important to examine entrepreneurial behavior in 2009 in context. In this chapter, we examine entrepreneurial behavior from 2005 to 2009 and describe how that analysis can inform our conclusions about what happened to entrepreneurial behavior in We believe that this contextual examination is particularly enlightening and can help address the reasons behind the results we find for In this chapter, we examine and analyze a number of dimensions of entrepreneurship from 2005 to Those dimensions include total entrepreneurial activity (TEA), age, work status, gender, education, household income, motivation, perception of entrepreneurial skills and fear of failure as an entrepreneur. The results indicate that, even though 2009 was a critical year in terms of entrepreneurial activity and behavior, there are some longer term trends, both positive and negative, occurring in U.S. entrepreneurial behavior. Moreover, those trends seem to have been exacerbated by the economic crisis of Following is a discussion of each of the areas. Trends in Total Entrepreneurship Activity Figure 3 presents the evolution of total entrepreneurship activity, owner-managed businesses and discontinued businesses from 2005 to While the TEA has been in an almost continuous decline for this period, punctuated by a significant fall from 10.6% in 2005 to 6.9% in 2009, the data for owner-managed businesses have held relatively stable for the period, and the data for discontinued business show a slow but steady decline for the period. The results indicate that fewer businesses are being started. However, of those businesses that have started, a higher percentage last more than 42 months. These results indicate that overall there might be more sustainability of businesses or perhaps these businesses are better capitalized. More importantly, these results hold through the 2009 crisis. Figure 3 Total Entrepreneurship Activity, Owner Managed Business and Discontinued Businesses, TEA Own-Manage Discontinued Businesses Source: GEM United States Adult Population Surveys (APS) An in-depth examination of the TEA shows different patterns for nascent and new businesses. Figure 4 shows the percentage of nascent and new businesses for the period from New businesses are those firms between 3 and 42 months of age, while nascent businesses are those firms that were started in the last three months or are planned for start-up in the next three months. While the start rates of nascent businesses shows a similar pattern to that of the TEA, the pattern for new business is different, showing an increase for the same period. 18

20 Entrepreneurial Behavior in the United States from 2005 to 2009: A Contextual View of the Effects of the Economic Crisis on Entrepreneurial Behavior Figure 4 Percentage of Nascent and New Businesses, % 90.0% 80.0% Nascent Business New Business 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Source: GEM United States Adult Population Surveys (APS) Total Entrepreneurship Activity and Gender Figure 5 shows total entrepreneurship activity by gender from An examination of the results regarding total entrepreneurship activity and gender indicate, first of all, a narrowing of the gap in TEA of males and females for the period. Even though the results for 2009 indicate a wider gap for that year, when taken in concert, the results for the whole period indicate that the gap in terms of TEA between males and females shows significant narrowing with women entrepreneurs now more than one-third of all entrepreneurs in The results also show that TEA by either gender decreases over time, but that the decrease seems to be smaller for women entrepreneurs. For owner managed business, the story is different. Both for male and female business owners, the results oscillate around a 1% band for the period, yet consistent with the results for TEA, the gap between males and female owner-managers also seems to be narrowing during the period, while the rate of discontinued businesses for males have been slightly decreasing over the time period analyzed. Figure 5 Total Entrepreneurship Activity and Gender, % 14.0% 12.0% TEA Male TEA Female Own-Manage Business Male Own-Manage Business Female Discontinued Business Male Discontinued Business Female 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Source: GEM United States Adult Population Surveys (APS)

21 Entrepreneurial Behavior in the United States from 2005 to 2009: A Contextual View of the Effects of the Economic Crisis on Entrepreneurial Behavior Total Entrepreneurship Activity and Age More interesting are the results regarding total entrepreneurship activity and age for the period. The difference between the 18 64, and age groups and the 45 54, and 65 & over age groups with respect to total entrepreneurship activity indicates a radical shift in terms of entrepreneurial activity in the United States. Whereas for the 18 24, and age groups, total entrepreneurial activity declined consistently over the period, the same is not true for the 45 54, and 65 & over age groups. Moreover, when combined, that is when examining the 45 & over age group, total entrepreneurship activity increased over the period, while for the group it decreased. The pattern in Figure 6 indicates a consistent and marked change in the entrepreneurial pattern in the United States. Traditionally, the age group has been the engine of entrepreneurial behavior in the United States, and even though entrepreneurial activity overall is still higher for this group, the gap in TEA between this age group and that of the 45 & over group is narrowing. If this tendency were to continue, the 45 & over age group would be the highest in terms of total entrepreneurship activity in the United States in the near future. The implications of this shift for a number of areas, from public policy to entrepreneurial education programs, will be important and should be considered by entrepreneurs, policy makers and academics. How, for example, would we structure entrepreneurial education in an environment in which most entrepreneurs were older? How, for instance, would the funding of new ventures be affected by these trends? It would be expected that older entrepreneurs are able to bring more personal funds, which they would have accumulated over time. Would this lead to better funded new ventures, and would it affect failure rates, since better funded ventures would possibly be less likely to fail? Is this one of the reasons behind the increased sustainability of businesses since 2005? All of these are questions that a shift in trends would bring to the fore for both public policy and research. Figure 6 TEA by Age Group, % 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 12.2% 10.6% 9.0% 10.2% 7.6% 15.5% 13.7% 12.2%12.3% 11.5% 15.3% 12.5% 11.2% 10.7% 10.3% 9.5% 9.5% 8.3% 7.6% 7.8% 7.2% 7.0% 5.7% 5.9% 5.4% 2.4% 1.2% 2.0% 2.4% 1.8% Years Years Years Years Years 65 Years and Over Source: GEM United States Adult Population Surveys (APS) The trends for nascent businesses are consistent with the overall TEA trends. We see consistent decreases over time in the 18 24, and age groups, and increases in rates for the 45 54, and 65 & over age groups. It is interesting to note the increase in the age group of from a little over 5% to over 10%. Traditionally, this group has not been examined or considered as a source of new venture creation. Further research should examine the role and the characteristics of this older age group in terms of entrepreneurial behavior. 20

22 Entrepreneurial Behavior in the United States from 2005 to 2009: A Contextual View of the Effects of the Economic Crisis on Entrepreneurial Behavior Figure 7 Percentage of Nascent and New Businesses by Age Group, % 35.0% Nascent Business New Business 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Years Years Years Years Years 65 Years and Older Years Years Years Years Years 65 Years and Older Years Years Years Years Years 65 Years and Older Years Years Years Years Years 65 Years and Older Years Years Years Years Years 65 Years and Older Source: GEM United States Adult Population Surveys (APS) Total Entrepreneurship Activity and Work Status A similar trend to that of age is occurring with work status. Over the period from 2005 to 2009, we see a marked decrease in entrepreneurs that work full time, from 14.7% to 8.5%; on the other hand, the TEA for percentage of entrepreneurs not working has doubled from 5.8% to 10.4%. This trend started before the Recession, showing a clear acceleration in Finally, the TEA numbers of students and retired individuals starting new businesses has hovered between 1.9% and 1.5% throughout this time period. The data for owner-managers for all three groups (working full time, not working and retired/students) does not show clear trends and has oscillated greatly throughout this period. Finally, the data for discontinued businesses shows some interesting trends. While for those working full time, the percentage of discontinued business has declined from 4.1% to 2.1%, for the period for those entrepreneurs not working, and for retired/students this is not as clear, remaining close to 2% for both groups. The trends for the period for nascent and new businesses are also not as clear, with a small decline in percentage for those working full time (83.3% to 65.1%), but no clear trends for those not working or for retired/student entrepreneurs. 21

23 Entrepreneurial Behavior in the United States from 2005 to 2009: A Contextual View of the Effects of the Economic Crisis on Entrepreneurial Behavior Figure 8 Work Status Over Time, % 14.0% 12.0% 10.0% TEA Own-Manage Business Discontinued Business 8.0% 6.0% 4.0% 2.0% 0.0% Work: Full-Time and Part-Time Not Working Retired Students Source: GEM United States Adult Population Surveys (APS) Figure 9 Percentage of Nascent and New Businesses by Work Status, % 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Nascent Business New Business Work: Full-Time, Part-Time Not working Retired/Students Work: Full-Time, Part-Time Not working Retired/Students Work: Full-Time, Part-Time Not working Retired/Students Work: Full-Time, Part-Time Not working Retired/Students Work: Full-Time, Part-Time Not working Retired/Students Source: GEM United States Adult Population Surveys (APS) Total Entrepreneurship Activity and Educational Status The data with respect to educational status and entrepreneurial activity as seen in Figures 10 and 11 show a number of interesting trends. First, the data indicate that those with post-secondary and graduate experience had higher TEA rates than business owner-managers. Entrepreneurial behavior seems to be higher for those with more education in this representative sample of U.S. residents. However, while the trends for both entrepreneurs with some secondary education and those with a secondary degree do not show clear trends for the period, the trends for those with post-secondary education and for those with graduate experience show a clear decline trend for the period, from 10.1% to 8.5% in TEA for those with post-secondary education, and from 12.3% to 9.4% for those with graduate education. It would be important in future surveys to examine this phenomenon to ascertain why this is the case. However, the data for owner-managers do not share this decline; there are no clear trends for the period. 22

24 Entrepreneurial Behavior in the United States from 2005 to 2009: A Contextual View of the Effects of the Economic Crisis on Entrepreneurial Behavior Figure 10 Educational Status Over Time, % TEA Own-Manage Business 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Some Secondary Education Secondary Education Degree Post-Secondary Education Graduate Education Experience Source: GEM United States Adult Population Surveys (APS) In terms of nascent and new businesses, the only area to highlight is the performance of those with graduate experience over the period. The number of nascent and new businesses started by those with graduate education have consistently increased over the period, more than double the rate those with secondary, some secondary, and post-secondary degrees. Although consistent with the overall TEA and ownermanager results, it is interesting to note the strength of the results of those with graduate degrees in terms of nascent and new businesses. It would also be interesting to examine the nature of the businesses created by those with graduate degrees. Are they more technology-oriented, better funded or better planned? Even though theoretically, those are issues that would be expected from businesses started by those with graduate degrees, it would be important to examine the data regarding those variables. We can examine the median cost of starting these businesses from 2005 to 2009 and see that this value has doubled from $15,000 median cost in 2005 to $30,000 median cost reported in

25 Entrepreneurial Behavior in the United States from 2005 to 2009: A Contextual View of the Effects of the Economic Crisis on Entrepreneurial Behavior 60.0% Figure 11 Percentage of Nascent and New Businesses by Educational Status, Nascent Business New Business 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% SOME SECONDARY EDUCATION SECONDARY EDUCATION DEGREE POST-SECONDARY EDUCATION GRADUATE EDUCATION EXPERIENCE SOME SECONDARY EDUCATION SECONDARY EDUCATION DEGREE POST-SECONDARY EDUCATION GRADUATE EDUCATION EXPERIENCE SOME SECONDARY EDUCATION SECONDARY EDUCATION DEGREE POST-SECONDARY EDUCATION GRADUATE EDUCATION EXPERIENCE SOME SECONDARY EDUCATION SECONDARY EDUCATION DEGREE POST-SECONDARY EDUCATION GRADUATE EDUCATION EXPERIENCE SOME SECONDARY EDUCATION SECONDARY EDUCATION DEGREE POST-SECONDARY EDUCATION GRADUATE EDUCATION EXPERIENCE Source: GEM United States Adult Population Surveys (APS) Total Entrepreneurship Activity and Income Tables 12 and 13 examine the relationship between total entrepreneurship activity, nascent and new businesses and household income for the period. The main issue to highlight with respect to income is the decline in TEA for both the middle and the upper third of household incomes. This decline is constant for each year of the period and goes from 13.9% to 5.8% for the middle third in household incomes and from 13.2% to a little under 10% for the upper third. However, in terms of owner businesses, the trends are not as clear, and new owner businesses oscillate around 7% for the middle third in household income, and around 10% for the upper third in terms of business owner income. The data for nascent and new business, on the other hand, shows an upward trend for the upper third in household income for both nascent and new business. Thus while for overall TEA, the upper third in household income shows a noticeable decline, the contrary occurs for nascent and new businesses. This is clearly an issue that has to be examined in more detail in future U.S. GEM analyses. First, to establish the continuity of those trends and, more importantly, to try to determine why that is the case. 24

26 Entrepreneurial Behavior in the United States from 2005 to 2009: A Contextual View of the Effects of the Economic Crisis on Entrepreneurial Behavior Figure 12 Household Income Over Time, % 14.0% 12.0% 10.0% TEA Own/Manage Business Discontinued Business 8.0% 6.0% 4.0% 2.0% 0.0% Lowest 33%tile Middle 33%tile Upper 33%tile Source: GEM United States Adult Population Surveys (APS) Figure 13 Percentage of Nascent and New Businesses by Household Income % 60.0% Nascent Business New Business 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Lowest Middle Upper Lowest Middle Upper Lowest Middle Upper Lowest Middle Upper Lowest Middle Upper Source: GEM United States Adult Population Surveys (APS) Entrepreneurs and their Motivations Surprisingly, given the changes we have described in terms of entrepreneurial activity from , there were little changes in terms of motivations of the entrepreneurs, and there were strong similarities between the three groups. The most important finding is that if you are starting or recently started a business, own/manage a business or have discontinued a business, you are significantly (p < 0.05) more likely to know someone who started a business, see good opportunities for starting a business and have the skills to start a business, and that trend is consistent for the whole period. More importantly, there were no differences between those that started a business, own-manage a business, or discontinued a business in terms of fear of failure. 25

27 Entrepreneurial Behavior in the United States from 2005 to 2009: A Contextual View of the Effects of the Economic Crisis on Entrepreneurial Behavior Figure 14 Entrepreneur Knows an Entrepreneur 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% TEA NO TEA YES Own/Manage Business NO Own/Manage Business YES Discontinued Business NO Discontinued Business YES Source: GEM United States Adult Population Surveys (APS) Figure 15 Entrepreneur Sees Good Opportunities for Starting a Business 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% TEA NO TEA YES Own/Manage Business NO Own/Manage Business YES Discontinued Business NO Discontinued Business YES 20.0% 10.0% 0.0% Source: GEM United States Adult Population Surveys (APS) 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% Figure 16 Entrepreneur Has Knowledge and Skills % 30.0% 20.0% 10.0% 0.0% TEA NO TEA YES Own/Manage Business NO Own/Manage Business YES Discontinued Business NO Discontinued Business YES Source: GEM United States Adult Population Surveys (APS)

28 Entrepreneurial Behavior in the United States from 2005 to 2009: A Contextual View of the Effects of the Economic Crisis on Entrepreneurial Behavior Final Comments The picture that emerges from the examination of TEA, owner managers, and nascent and new businesses for the period from is one of lights and shadows. The data indicate that fewer businesses are being started and that is a troubling trend, yet those that get started appear to be more sustainable. Finally, the percentage of discontinued businesses has held stable for the period, which again attests to the sustainability of those businesses started. Gender and age also plays an important role, and while traditionally more males than females start businesses in the United States, our results indicate that the gap is narrowing with respect to TEA and owner businesses. More significant is the shift in terms of age and new venture creation from the age groups, traditionally viewed as the entrepreneurial motor of the U.S. to the 45 and over group. This shift has important implications for our understanding of the entrepreneurial process, entrepreneurial education, and public policy. It warrants an in-depth examination of the drivers of that phenomenon and the implications it has for the new ventures being created. Finally, even though in the data on new venture creation, work and educational status, and income, the results are not as striking, some interesting trends emerge from the data. Decreases in TEA for those working full time, and increases in entrepreneurial activity in those with post-secondary and graduate experience are particularly salient. While it is reasonable to believe the reduction in entrepreneurial activity could, to some extent, be attributed to a more risky global environment and the accompanying perception of risk by entrepreneurs, GEM data shows very little change during this period. Further research is needed to explore the possible causes. Our results also indicate that there were no differences between those that started a business, owned-managed a business, or discontinued a business in terms of fear of failure. Thus, having discontinued a business does not make you more fearful of failure than those that have started or own a business. It seems that the American entrepreneurial approach of second chances and discounting failure is still alive and well. 27

29 2 International Comparison: The United States and Other Countries Ivory Phinisee and Al Suhu GEM Participating Countries in 2009 In total, 54 countries participated in the GEM project in The countries in this report are grouped into three stages of economic development as defined by the World Economic Forum s Global Competitiveness Report: Factor-Driven, Efficiency- Driven and Innovation-Driven. This classification in stages of economic development is based on the level of GDP per capita. Factor-Driven Economies Algeria,* Guatemala,* Jamaica,* Lebanon,* Morocco,* Saudi Arabia, Syria,* Kingdom of Tonga, Uganda, Venezuela,* West Bank and Gaza Strip, Yemen Efficiency-Driven Economies Argentina,* Bosnia and Herzegovina, Brazil, Chile,* China, Colombia, Croatia,* Dominican Republic, Ecuador, Hungary,* Iran, Jordan, Latvia,* Malaysia, Panama, Peru, Romania*, Russia,* Serbia, South Africa, Tunisia, Uruguay* Innovation-Driven Economies Belgium, Denmark, Finland, France, Germany, Greece, Hong Kong, Iceland, Israel, Italy, Japan, Republic of Korea, Netherlands, Norway, Slovenia, Spain, Switzerland, United Arab Emirates, United Kingdom, United States *Country in transition to more advanced stage This chapter answers two questions: First, using the GEM 2009 data for all 54 countries, it answers the question of how current entrepreneurial activity in the United States compares to other countries. Second, using a subset of GEM countries, 1 it uses time series analysis to assess and compare the impact the global recession may have had on entrepreneurship globally and in the United States. The analyses will be split into three dimensions of entrepreneurship from the GEM Model: Activity, Attitudes and Aspirations. The subset of GEM countries used in the time series analysis have participated in GEM since 2001 missing at most one year of data. 2 Fifteen innovationeconomy countries had sufficient data to be included, as do six countries from efficiency-driven economies. Therefore, these results should be interpreted with care. ACTIVITY Entrepreneurial Activity Table 2 summarizes the components of total entrepreneurial activity for each of the GEM 2009 countries. Of innovation-driven economies, the United States is among the countries with the highest prevalence rates for nascent entrepreneurial activity and is higher than the average for TEA (total early-stage entrepreneurial activity). However, when measuring established business prevalence rates, the United States compares less favorably, falling below the average in all three economies (factor-, efficiencyand innovation-driven). In addition, early-stage entrepreneurial activity in the United States is below the average in factor-driven and efficiency-driven economies. Entrepreneurial Motivations The GEM definition of improvement-driven opportunity entrepreneurial activity is: The percentage of the early-stage entrepreneurs that are involved in entrepreneurship because: (i) they claim to be driven by opportunity as opposed to finding no other option for work; and (ii) they indicate the main driver for being involved in this opportunity is being independent or increasing their income, rather than just maintaining. The 2009 U.S. improvement-driven opportunity early-stage entrepreneurial activity expressed as a percentage of total early-stage activity (%TEA) is higher than the averages of the factor- and efficiency-driven economies. However, in 2009, the U.S. improvement-driven opportunity activity declined to 55% of its TEA, a drop of over 10% from The U.S. percentage of total early-stage 28 1 This concerns the following efficiency-driven countries: Argentina, Brazil, Chile, Croatia, Hungary and South Africa. Innovation economies include: Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Italy, Japan, Netherlands, Norway, Slovenia, Spain, United Kingdom and United States of America. 2 Missing data have been estimated as a function of the existing data.

30 International Comparison: The United State and Other Countries Table 2 Prevalence Rates (in %) of Entrepreneurial Activity and Business Owner-Managers Across GEM Countries in 2009, Age Group, by Phase of Economic Development Nascent Entrepreneurship Rate % New Business Ownership Rate % Early-Stage Entrepreneurial Activity (TEA) % Established Business Ownership Rate % Discontinuation of Businesses % Necessity-Driven (% of TEA) Improvement- Driven Opportunity (% of TEA) Factor-Driven Economies Algeria Guatemala Jamaica Lebanon Morocco Saudi Arabia Syria Tonga Uganda Venezuela West Bank & Gaza Strip Yemen average (unweighted) Efficiency-Driven Economies Argentina Bosnia and Herzegovina Brazil Chile China Colombia Croatia Dominican Republic Ecuador Hungary Iran Jordan Latvia Malaysia Panama Peru Romania Russia Serbia South Africa Tunisia Uruguay average (unweighted) Innovation-Driven Economies Belgium Denmark Finland France Germany Greece Hong Kong Iceland Israel Italy Japan Korea Netherlands Norway Slovenia Spain Switzerland United Arab Emirates United Kingdom United States average (unweighted) Source: GEM Global 2009 Adult Population Survey (APS). For international comparisons, sample based on Age Group. 29

31 International Comparison: The United States and Other Countries entrepreneurs involved in necessity-driven earlystage entrepreneurship (23%) is higher than the average for innovation-driven economies but is lower than the averages of both efficiency-driven and factordriven economies in Discontinuing Business The U.S. business discontinuation rate was higher than the average for the innovation-driven countries in The U.S. rate, however, is much lower than the averages of both efficiency-driven and factordriven economies in The primary reason given in 2009 for businesses discontinuance among the three phases of economic development and the U.S. was businesses were not profitable. Prior to the recession, entrepreneurs stated personal reasons as the number one reason for business discontinuance both in the U.S and globally. Entrepreneurial Activity by Sector Distributions As shown in Figures 17 and 18, classified by industry sector, the early-stage entrepreneurial activity and established business owner-manager rates follow an expected pattern across the three phases of economic development. Extraction businesses (farming, forestry, fishing and mining) are expected to be more prevalent in factor-driven economies. Transforming businesses (manufacturing and construction) are expected to be more prevalent in efficiency-driven economies. Business services should be more prevalent in innovation-driven economies. Furthermore, the proportion of consumer-oriented businesses should decline as a country transitions to higher phase(s) of economic development. Countries with poorly developed transportation and commercial infrastructure tend to have higher proportions of consumer-oriented businesses because they do not have the means to buy or ship goods or services beyond the immediate region, thus limiting their customer and supply base. Economies tend to grow more when business-to-business activity is established. The United States has a noticeably smaller proportion of consumer-oriented businesses than the averages of the three economic development phases when considering both early-stage entrepreneurial activity and established business owner-managers. In Figures 17 and 18, the proportion of business services sector in the United States is much larger than the averages of the three phases of economic development. Figure 17 Sector Distribution Early-Stage Entrepreneurial Activity (18 64 Age Group), 2009 Factor-Driven Economies Extractive Efficiency-Driven Economies Innovation-Driven Economies Transforming Business Services Consumer-Oriented United States 0% 20% 40% 60% 80% 100% Source: GEM Global 2009 Adult Population Survey (APS). For international comparisons, sample based on Age Group. 30

32 International Comparison: The United States and Other Countries Figure 18 Sector Distribution Established Business (18 64 Age Group), 2009 Factor-Driven Economies Extractive Efficiency-Driven Economies Innovation-Driven Economies Transforming Business Services Consumer-Oriented United States 0% 20% 40% 60% 80% 100% Source: GEM Global 2009 Adult Population Survey (APS). For international comparisons, sample based on Age Group. Entrepreneurial Activity by Age Group As shown in Figure 19, the shapes of the age distributions are very similar across the averages of the three economic phases of development for earlystage entrepreneurial activity, with the age range having the highest prevalence rates. The U.S. age distribution also follows a similar age pattern but with approximately equal prevalence rates between the age range and the age range the age range with the age range. This indicates that unlike the global averages, in the United States, there is a higher ratio of early-stage entrepreneurial activity in the age range relative to the age range. 25% Figure 19 Early-Stage Entrepreneurial Activity for Separate Age Groups, Years Years Years Years Years 20% Percentage of Adult Population in Age Group 15% 10% 5% 0% Factor-Driven Economies Efficiency-Driven Economies Innovation-Driven Economies Source: GEM Global 2009 Adult Population Survey (APS). For international comparisons, sample based on Age Group. United States 31

33 International Comparison: The United States and Other Countries Trends in the Total Early-Stage (TEA) Entrepreneurial Activity The components of total entrepreneurial activity that will be examined in this section are total early-stage activity (TEA) and owners-managers of an established business (more than 3.5 years old). TEA consists of two components: nascent entrepreneurs (involved in setting up a business) and owner-managers of a new business (up to 3.5 years old). Trends will be used for comparative analysis between the United States and the averages of phases of entrepreneurship for different stages of entrepreneurial activity. Figure 20 is a chart of the average annual TEA rates from for the United States and a subset of the GEM efficiency-driven and innovationdriven countries explained above. The TEA rates for the average of the innovation-driven economies have been stable at around a 6% rate since The TEA rates for the efficiency-driven economies have shown a steady upward trend since 2005 and have been on average higher than the corresponding rates for innovation-driven economies. The U.S. TEA rates have been trending downward since 2005, punctuated by the significant fall in In the past three years, the U.S. rates have fallen to a level lower than the average TEA rate for efficiencydriven economies in These results indicate that the global recession has affected the U.S. earlystage entrepreneurial activity more severely than early-stage entrepreneurial activity in the average efficiency-driven and innovation-driven countries throughout the world. Figure 20 Total Early-Stage Entrepreneurial Activity (TEA) Rates, , Averages Over Efficiency-Driven Countries, Innovation-Driven Countries and the United States Innovation-Driven Economies Efficiency-Driven Economies United States 14% 12% 10% 8% 6% 4% 2% 0% Source: GEM Adult Population Surveys (APS) 32

34 International Comparison: The United States and Other Countries Figure 21 illustrates that the nascent entrepreneurial activity rate, a component of the TEA, declined by nearly 45% in the United States, going from 8.7% of the U.S. adult population in 2005 to 4.9% in Nascent entrepreneurial activity in the United States exceeded that of the average of the efficiency-driven and innovation-driven economies prior to High levels of early-stage entrepreneurial activity promote efficiency in the economy and drive exploitation of new market opportunities. 3 This downward trend in the U.S. early-stage entrepreneurial activity was exacerbated by the housing market s bubble bursting in 2006, whose destruction contributed to the financial crisis leading to a global recession. A sign that the United States has recovered from this financial crisis would be a reversal of the downward trend in the activity of the nascent entrepreneur. The U.S. established business prevalence rate slipped considerably from 8.3% in 2008 to 5.7% in At 5.7%, the U.S. established business ownership rate fell below the averages of both the efficiency-driven and innovation-driven countries 2009 rates. The recession seems to have more negatively affected the United States than the efficiency-driven and innovation-driven economies in terms of prevalence rates for established business during the period from 2008 to 2009 (see Appendix Table 1). A parallel situation exists with the U. S. TEA rates compared to the efficiency-driven and innovation-driven economies. Figure 21 Nascent Entrepreneurial Activity Rates, , Averages Over Efficiency-Driven Countries, Innovation-Driven Countries and the United States Innovation-Driven Economies Efficiency-Driven Economies United States 10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0% Source: GEM Adult Population Surveys (APS) 3 Schumpeter argued that when innovative new firms were allowed to compete they would use their creativity to destroy older firms that did not match up. Therefore, social well-being as a whole is improved. 33

35 International Comparison: The United States and Other Countries Trends in Entrepreneurial Motivations The rates of necessity-driven TEA were, on average, stable from 2005 to However, the recession caused an increase in the necessity-driven TEA rates for the United States and for the average of the efficiency-driven economies in 2009, as illustrated in Figure 22. The U.S. improvement-driven opportunity TEA rate dropped below the rate for the average of the efficiency-driven economies for the first time in The U.S. improvement-driven opportunity remained higher than the average for the innovationdriven economies from 2005 to 2009, but the gap between their improvement-driven opportunity rates was significantly reduced in 2009 (see Appendix Table A1). Figure 22 Necessity-Driven Opportunity TEA Rates, , Averages Over Efficiency-Driven Countries, Innovation-Driven Countries and the United States Innovation-Driven Economies Efficiency-Driven Economies United States 10 % 8 % 6 % 4 % 2 % 0 % Source: GEM Adult Population Surveys (APS) Total Early-Stage Entrepreneurial Activity and Gender Figure 23 shows total early-stage entrepreneurship activity by gender from 2005 to An examination of the results for the United States indicates that the gap between male and female TEA rates narrowed during the period from 2005 to The gaps in the TEA between males and females for the averages of the efficiency-driven and innovation-driven countries are similar to that of the United States, hovering around 4%. The United States males and the average of the males in the efficiency-driven countries had the highest and similar TEA rates between 2007 and The males in the early-stage in the efficiencydriven economies continued to trend upward in their TEA in 2009 in spite of the recession. This was not the case for the U.S. early-stage gender activity, as both the males and females in the U.S. experienced significant declines in their TEA rates in The gap in the average ratio of male to female participation increases across the phases with lows of 1.4 males to females in the average efficiencydriven economies to twice as many males involved in early-stage entrepreneurial activity as women in innovation-driven countries in The gap is smaller in the United States, on the other hand, with more women involved in early-stage entrepreneurial activity relative to the average rate in innovationdriven countries. 34

36 International Comparison: The United States and Other Countries Figure 23 Total Early-Stage Entrepreneurship by Gender Percentage of Adult Population in Age Group 18% 16% 14% 12% 10% 8% 6% 4% Male Efficiency Female Efficiency Male Innovation Female Innovation Male United States Female United States 2% 0% Source: GEM Adult Population Surveys (APS) ATTITUDES Figures in this section display the trends of the many dimensions of entrepreneurial attitudes for the United States and compare them with the averages of a subset of efficiency-driven and innovation-driven countries over the period from 2005 to Looking at the percentage of entrepreneurs who perceived good opportunities for starting a business in the next six months in the area where they lived, the average for the efficiency-driven economies was higher than the United States and higher than the average for innovation-driven economies (Figure 24). This is predictable since the average of the efficiency-driven economies have highest early-stage entrepreneurial prevalence rates. The recession may be responsible for the negative progression of the average of the efficiency-driven economies for 2008 and for the averages of the United States and innovation-driven economies in As shown in Figure 25, the trend for fear-of-failure in the United States closely mirrors that of the average for innovation-driven and efficiency-driven economies. However, the United States attitude has been approximately 12 percentage points lower than the average over the past eight years. This clearly shows a large gap in attitudes regarding the fear of failure between the average U.S. entrepreneur and the averages of the innovation-driven and efficiencydriven economies. When examining the strength of the entrepreneurial ethos in the United States, scholars have centered on the notion that less concern for failure would be a key driver of entrepreneurial behavior in the United States. In this case and when comparing with its country group peers, the results indicate there is less fear of failure in the United States. However, the United States bagan showing an upward trend in fear of failure beginning in 2006 and continuing through the recession in Figure 26 shows the trends for entrepreneurs confidence in having sufficient knowledge and skills to start a business. The average of innovationdriven economies has been relatively stable over the past five years at around 40%. Efficiencydriven economies averaged over 50% over the same period. The U.S. attitudes had been declining from but rebounded in 2008 and exceeded the average of innovation-driven countries by about 20 percentage points. All in all, results indicate that U.S. entrepreneurs are very confident in having the required knowledge and skills to start a business. From Figure 27, it is clear that the expectation to start a business within three years has been around 10% for innovation-driven countries. The average for efficiency-driven countries shows an upward trend over the past five years and is about 10 percentage points higher than the average for innovation-driven countries over the past three years. The average sentiment in the United States shows a declining trend from This decline in the expectation to start a business within three years may reflect a deep-seated concern in the United States with the conditions around new venture creation. It is important to track and analyze this key GEM variable in the future. 35

37 International Comparison: The United States and Other Countries Figure 24 Perceived Opportunities for Starting a Business, Innovation-Driven Economies Efficiency-Driven Economies United States 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Source: GEM Global 2009 Adult Population Survey (APS). For international comparisons, sample based on Age Group. 36

38 International Comparison: The United States and Other Countries Figure 25 Fear of Failure Among Those Who Perceive Good Start-Up Opportunities, Innovation-Driven Economies Efficiency-Driven Economies United States 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Source: GEM Global 2009 Adult Population Survey (APS). For international comparisons, sample based on Age Group. 37

39 International Comparison: The United States and Other Countries Figure 26 Perceived Skills and Knowledge to Start a New Business, Innovation-Driven Economies Efficiency-Driven Economies United States 60% 50% 40% 30% 20% 10% 0% Source: GEM Global 2009 Adult Population Survey (APS). For international comparisons, sample based on Age Group. 38

40 International Comparison: The United States and Other Countries Figure 27 Intentions to Start a New Business in the Next Three Years, Innovation-Driven Economies Efficiency-Driven Economies United States 25% 20% 15% 10% 5% 0% Source: GEM Global 2009 Adult Population Survey (APS). For international comparisons, sample based on Age Group. ASPIRATIONS One of the measures GEM uses to assess entrepreneurial aspirations is job creation. As stated in the GEM 2008 Global Executive Report, highgrowth entrepreneurs, also known as gazelles, receive a great deal of attention from policy makers because their firms contribute a disproportionate share of all new jobs created by new firms. GEM defines high-growth entrepreneurs (HEA) as those entrepreneurs who expect to have 20 or more employees (other than the owners) within the next five years. Figure 28 shows the rate of high-growth expectation for early-stage entrepreneurship in GEM countries for which a sufficient sample size was available, grouped by level of economic development. The United States has the ninth highest rate among the 24 innovation-driven countries and is above the average within the innovation-driven grouping. Compared with efficiency-driven and factor-driven countries, the U.S. rate is approximately equal to the averages of those groups. 39

41 International Comparison: The United States and Other Countries Figure 28 High-Growth Expectation Early-Stage Entrepreneurial Activity by Country (18 64 Age Group): % COUNTRY MEAN PHASE AVERAGE 50% Percentage of Early-Stage Entrepreneurs 40% 30% 20% 10% 0% Jamaica India Venezuela AVERAGE Brazil Hungary Peru Dominican Serbia Chile Croatia Argentina Romania Latvia Russia AVERAGE Spain Finland Greece Belgium Netherlands France Germany Norway Italy Switzerland Australia Japan Ireland Sweden Slovenia United States United Kingdom Canada Denmark Iceland Singapore Israel Hong Kong United Arab Emirates AVERAGE Factor-driven Efficiency-driven Innovation-driven Source: GEM Global 2009 Adult Population Survey (APS) The Impact of the Recession on Entrepreneurial Activity According to the Entrepreneurs Figures 29 and 30 shows the percentage of responses from entrepreneurs on how they perceive opportunities for starting and growing a business in the current year compared to a year ago. The responses were summarized by type: nascent entrepreneur, new business owner and established business owners within each of the three phases of economic development and the United States. Over 50% of the entrepreneurs answered that it was more difficult to start a business now compared to a year ago. This was true for the United States and the three phases of economic development and for the nascent entrepreneur, new business owner and the owner of an established business. Figure 30 displays the summary of the answers by the entrepreneurs to the question about growing (rather than starting) a business now compared to one year ago. The nascent entrepreneurs and the owner-managers of established businesses responded most that it was more difficult now to grow a business than a year ago. This was true also for entrepreneurs in the four categories: factor-driven, efficiency-driven, and innovation-driven economies and the United States. The owner-managers of a new business were nearly even in their responses of whether it was more difficult or less difficult to grow a business now compared to a year ago. The data show that starting a business is more difficult in general than growing an existing business. Figure 31 displays the results to the question that asked the entrepreneurs directly about the global economic crisis and its impact on opportunities for their businesses. The overwhelming majority of responses by the entrepreneurs were that there are fewer business opportunities because of the impact of the global recession. This was true among all the economic phases of development and stages of entrepreneurship See Acs, 2008; Autio, 2007.

42 International Comparison: The United States and Other Countries Figure Entrepreneurs Views on Starting a Business in Comparison to One Year Ago by Phase of Economic Development (Unweighted Country Averages) %Yes 90% 80% 70% Factor-Driven Economies Innovation-Driven Economies Efficiency-Driven Economies United States 60% 50% 40% 30% 20% 10% 0% Less Difficult About the Same More Difficult Less Difficult About the Same More Difficult Less Difficult About the Same More Difficult Nascent Entrepreneurs Owner-Managers of New Businesses Owner-Managers of Established Businesses Source: GEM 2009 Adult Population Survey (APS) Figure Entrepreneurs Views on Growing a Business in Comparison to One Year Ago by Phase of Economic Development (Unweighted Country Averages) %Yes 60% 50% Factor-Driven Economies Innovation-Driven Economies Efficiency-Driven Economies United States 40% 30% 20% 10% 0% Less Difficult About the Same More Difficult Less Difficult About the Same More Difficult Less Difficult About the Same More Difficult Source: GEM 2009 Adult Population Survey (APS) Nascent Entrepreneurs Owner-Manager of New Businesses Owner-Manager of Established Businesses 41

43 International Comparison: The United States and Other Countries Figure 31 Impact of the Global Economic Slowdown on Entrepreneurs Perception of Opportunities for Their Business, According to the Entrepreneurs (Unweighted Country Averages) %Yes 80% 70% Factor-Driven Economies Innovation-Driven Economies Efficiency-Driven Economies United States 60% 50% 40% 30% 20% 10% 0% Fewer Business Opportunities About the Same More Business Opportunities Fewer Business Opportunities About the Same More Business Opportunities Fewer Business Opportunities About the Same More Business Opportunities Source: GEM 2009 Adult Population Survey (APS) Nascent Entrepreneurs Owner-Managers of New Businesses Owner-Managers of Established Businesses CONCLUSION BIBLIOGRAPHY In general, the global recession has had a negative impact on entrepreneurial activity globally and in the United States. It appears that the impact was more severe in the United States than in the efficiencyand innovation-driven economies as a whole. Entrepreneurial attitudes and perceptions have been negatively impacted by the global recession. There is a greater fear of failure to start businesses now, and entrepreneurs report overwhelmingly that there are fewer business opportunities because of the impact of the global recession. Acs, Z. J., W. Parsons and S. Tracy (2008). High Impact Firms: Gazelles Revisited. Office of Advocacy, U.S. Small Business Administration. Autio, E. (2007). Global Entrepreneurship Monitor 2007 Global Report on High Growth Entrepreneurship. London, U.K.: London Business School and Babson Park, MA: Babson College. Kuznets, S. (1966b). Modern Economic Growth Rate Structure and Spread. New Haven and London: Yale University Press. Schumpeter, J. A. (1942). Capitalism, Socialism and Democracy, New York: Harper and Row. 42

44 3 Does Innovation Drive Entrepreneurship? Abdul Ali The intimate association of innovation with corporate renewal and new venture creation has been well researched by academic scholars. Innovations enable entrepreneurs to start or grow their businesses, and in the process such endeavors not only create wealth for these individuals, but also potentially generate employment for others,, which in turn helps develop the economy of nations. Countries all over the world are now paying greater attention to the linkage between innovation and entrepreneurship. Further, as countries becomes more prosperous, innovation and consequently entrepreneurial activities turn out to be the most effective, if not the only, way to enhance the social welfare of their citizens. The United States is an innovation-driven economy and, as such, must understand and support the innovative activity of its entrepreneurs to maintain an economic edge in the world. In fact, it is imperative to know how U.S. entrepreneurs are coping with the need for innovation in the aftermath of the economic downturn of The GEM 2009 survey for the United States has asked questions about innovativeness of products and services, involvement in the technology sector, use of new technology and intended expenditures on new technology. The findings follow. INNOVATIVENESS AND CUSTOMER NOVELTY As the GEM Global 2009 Executive Report notes, GEM uses two different ways to assess innovation in entrepreneurial businesses. One is product oriented and the other is measuring business originality. For the first, a product or service developed by an entrepreneur is considered to be innovative if the target customers find the product or service unfamiliar or novel relative to their current experiences (product novelty). The second relies on the innovativeness of an entrepreneurial business as measured by the degree of competitiveness faced by the business; whether the owner-manager perceives that few or no other businesses offer similar products or services (market newness). Figure 32 compares the 2007 and 2008 data of the relative prevalence of early-stage entrepreneurs and established business owner-managers offering novel product-market combinations. Clearly, both groups exhibit a declining trend. The product offerings are less innovative in 2009 compared to the previous year. The decline in product innovativeness, however, was steeper for established business owners (26.1% fewer established businesses reported developing innovative products in 2009 compared to 2008; the corresponding drop for entrepreneurs is only 7.9%). Developing fewer novel products or services by U.S. entrepreneurs may reflect the worsening economic situation in 2009, which is discussed next. Figure 32 Percentage of Business Entities with New Product-Market Combinations 30.0% 25.0% Percentage of business Entities with New Product-Market Combinations 20.0% 15.0% 10.0% Early-Stage Entrepreneurial Activity Established Business 5.0% 0.0% Source: GEM U.S and 2009 Adult Population Surveys (APS) 43

45 Does Innovation Drive Entrepreneurship? Impact of Economic Outlook on Innovation in 2009 The GEM 2009 survey for the United States asked specific questions on the impact of the global recession on entrepreneurship. Entrepreneurs were asked about their views on the effect of the global economic downturn on business opportunities for their business entities. Two additional questions were asked about entrepreneurs perceptions of the climate in mid-2009 for starting and growing a business. Figure 33 displays perceptions of entrepreneurs and established business owners of the 2009 economic outlook for finding opportunities in starting and growing businesses and how their perception impacted their commitment to developing and marketing innovative products. The intent behind asking these questions in this way is to understand the degree to which perceived economic outlook influenced the new product development activities of business entities. In general, early-stage entrepreneurs are more positive about the 2009 economic outlook compared to established business owners. While 54.5% of entrepreneurs found it more difficult to start a business in 2009 compared to 2008, established business owners were significantly less optimistic (75.6% felt difficulty in starting a business last year). A similar pattern can be observed across the other two indicators of economic outlook (growth expectations and business opportunities). It must be noted that both classifications of business entities were more positive about growing a business than about starting one. Only 39.6% of the entrepreneurs have lower expectations for growth in 2009 compared to last year, whereas fully 54.5% of the same group felt it would be more difficult to start a business in 2009 than in For established business owners, the rates are 54.1% and 75.6% respectively for the same. Figure 33 also provides information on the entrepreneurs and established business owners views of how the 2009 economic downturn affected their commitment to developing innovative products. On the whole, more early-stage entrepreneurs, who share the same grim economic outlook of the established business owners for 2009, are developing new product-market combinations than their counterparts in the established business sector (7 to 11.7% of the entrepreneurs compared to 4.7 to 7.4% of the established business owners across the three economic outlook indicators). Such positive commitment to innovation may serve these entrepreneurs well once the economic crisis is over in near future. Figure 33 Impact of Business Entities Perceptions of Economic Outlook on Innovation 80.00% 70.00% Percentage of Business Entities 60.00% 50.00% 40.00% 30.00% 20.00% SU: Start-Up EB: Established Business 10.00% 0.00% SU EB SU EB SU EB Difficult to Start Business Source: GEM U.S 2009 Adult Population Survey (APS) Low Expectation for Growth Fewer Business Opportunities 44

46 Does Innovation Drive Entrepreneurship? In addition to measuring innovative entrepreneurial activity in terms of customer unfamiliarity and lack of competitive intensity, GEM also assesses the percentage of business entities reporting they need the latest technologies or procedures for their new products or services. Both types of business entities reported needing the latest technologies less in 2009 than in Fewer early-stage entrepreneurs indicated a need for the latest technologies or procedures in 2009 than in 2008 (4.1% and 5.9% respectively) and for the same two years, less than 1% of established business owners reported such a need. Given the economic outlook of , it seems both early-stage entrepreneurs and established business owner-managers have cut back on developing more risky novelty products using the latest technology. It can then be surmised that both business entities continued to report more incremental innovations as opposed to radical new products in ACTIVITY IN TECHNOLOGY SECTOR Given the fact that the United States has an innovation-driven economy, it is expected that new technology will be the main driver of innovation in most cases, and business entities will be active in the technology sector of the economy. However, the dismal economic outlook of 2009 leads one to predict a significant drop from the 2008 number of established and early-stage businesses involved in the technology sector. Unexpectedly, U.S. entrepreneurs (nascent and early-stage) are the ones getting less involved in the high-tech sector compared to established business owners and the reason is due to the higher degree of risk and the expensive nature of technological innovation. Figure 34 presents these percentages: Early-stage entrepreneurs active in the technology sector dropped from 7.8% in 2008 to 1.7% in The corresponding numbers for established business owner-managers are 5.2% in 2008 and 3.1% from In 2009, starting as an Internet business was a more popular option for the early-stage entrepreneur than it was for the established business owner. Early-stage entrepreneurs reported starting out as an Internet business 18% of the time in 2009, as opposed to 2.3% a year earlier. Only 0.5% of established business owner-managers started Internet businesses in 2009, compared to 6.8% in Continued innovation of the Internet (e.g., Web 2.0) has created new opportunities that early-stage entrepreneurs have seized to start their businesses regardless of whether their business involved technology-intensive products or services. Figure 34 Percentage of Business Entities Active in Medium- or High-Technology Sector 9.0% 8.0% Percentage of Business Entities Active in Technology Sector 7.0% 6.0% 5.0% 4.0% 3.0% 2.0% 1.0% % Early-Stage Entrepreneurial Activity Established Business Source: GEM U.S and 2009 Adult Population Surveys (APS) 45

47 Does Innovation Drive Entrepreneurship? SPENDING ON TECHNOLOGY It is expected that, in order to develop an innovative product, a firm needs to spend money on R&D and new technology. Intention to spend on technology provides a clear indication of the innovation strategy of entrepreneurs. It would be expected that companies might cut back on R&D spending in 2009, given the economic climate, compared to what they spent in the previous year (see Figure 35). While almost three-fourths of both types of business entities reported their willingness to spend less than $5,000 in 2009 (the number remains unchanged from the previous year), a significantly larger number of established business owners than early-stage entrepreneurs expressed their intention to cut back on R&D spending over $20,000 in 2009 compared to 2008 (13.7% in 2008 and 5.9% in 2009 for established business owners; such numbers for entrepreneurs are 11.6% and 10.2% respectively). Given the economic crisis of the past two years, it was a very encouraging sign of early-stage entrepreneurial activity in 2009 that entrepreneurs, unlike established business owners, were willing to spend a large sum (over $20,000) on R&D and such endeavor may hopefully lead to more innovative products in the future. Figure 35 Percentage of Business Entities Spending on Technology in % 8.0% Percentage of Business Entities Active in Technology Sector 7.0% 6.0% 5.0% 4.0% 3.0% 2.0% 1.0% % Early-Stage Entrepreneurial Activity Established Business Source: GEM U.S and 2009 Adult Population Surveys (APS) FINDING BUSINESS SUCCESS Given the innovation-driven economy of the United States, it would be interesting to know how various activities undertaken by business entities lead to success. In the GEM survey for the United States, business entities were specifically asked whether they would define their business as a success. Figure 36 displays entrepreneurs and established business owners agreement about defining their business as a success. In general, both business entities were less confident about identifying their business a success in 2009 than they were in It must be noted that GEM evidence finds that, in general, early-stage entrepreneurs were less willing to define their business as a success compared to established business owners. While only 69.1% of entrepreneurs called their businesses a success in 2009, a significantly higher number (91.4%) of established business owners defined so in the same year. Such caution on the part of entrepreneurs may be attributed to the fact their businesses are only a few months old, while established businesses have some history to help them form opinions. 46

48 Does Innovation Drive Entrepreneurship? Figure 36 Percentage of Business Entities Finding Success 9.0% 8.0% Percentage of Business Entities Active in Technology Sector 7.0% 6.0% 5.0% 4.0% 3.0% 2.0% 1.0% % Early-Stage Entrepreneurial Activity Established Business Source: GEM U.S and 2009 Adult Population Surveys (APS) In summary, the GEM 2009 data suggest that earlystage entrepreneurs report less involvement in the technology sector, compared to established business owners. However a larger number of early-stage entrepreneurs were committed to developing more innovative products, starting an Internet business, spending more than $20,000 on technology in 2009 and feeling more optimistic about the 2009 economic outlook. Yet entrepreneurs were more cautious about calling their business a success. Such cautiously optimistic approaches may serve these entrepreneurs well once the economic crisis of is over. 47

49 4 Social Entrepreneurship John R. Whitman The GEM 2008 data indicated increasing interest in social entrepreneurship as an approach to business start-up and growth and in 2009 social entrepreneurship continues to capture the imagination of the world. Yet despite the growing evidence and legitimacy of social entrepreneurship, a generally agreed upon definition remains elusive. Virtually every treatment of social entrepreneurship opens with the disclaimer that there is no single definition of social entrepreneurship. This section concerning social entrepreneurship thus makes no normative claims about definition. No definition of social entrepreneurship was given to respondents of the Adult Population Survey, nor were respondents asked to self-identify as social entrepreneurs. Rather than present a (topdown) definition of social entrepreneurship and ask respondents to self-identify as such, the GEM survey asked about the social and environmental dimensions of businesses and thus created a construct (bottom up) called social entrepreneurship. Thus while we can make no statements about self-identified social entrepreneurs based on the 2009 data, we can report on what entrepreneurs say about the social and environmental dimensions of their ventures. We will first compare selected results from the 2008 and 2009 surveys and comment on variations, particularly in reference to the recent recession. Then we will turn our attention to questions introduced in the GEM 2009 survey that may shed further light on understanding businesses that have a particularly social, environmental or community objective. THE KEY QUESTIONS There are two sets of questions that focus on the social dimensions of businesses. The first set, Goals and Values, consists of two questions listed sequentially and posed separately to new business start-ups and other established businesses: 2) Organizations may have goals according to their ability to generate economic value, societal value and environmental value. Please allocate a total of 100 points across these three categories as they pertain to your goals. For example, an organization goals may allocate 80 points for economic value, 10 points for societal value and 10 points for environmental value. Economic value Societal value Environmental value The second set of questions, Social, Environmental and Community Objective, is posed to all survey respondents who answer affirmatively to the following question: Are you, alone or with others, currently trying to start or currently owning and managing any kind of activity, organization or initiative that has a particularly social, environmental or community objective? This might include providing services or training to socially deprived or disabled persons, using profits for socially oriented purposes, organizing self-help groups for community action, etc. Each set of questions will be discussed in the sections that follow, after a brief comparison of 2009 to earlier results. GOALS AND VALUES The 2008 survey is the first to provide a reasonable basis for comparison of selected questions in the 2009 survey. Beginning with goals, Figure 37 shows the distribution of responses in 2008 and 2009 for startups that involve financing by at least one other source in addition to the founder. 1) Which of the following best describes the goals of your business? (choose one) For profit primarily achieving economics goals For profit primarily achieving social goals For profit equally emphasizing social and economic goals Not for profit serving a social mission 48

50 Social Entrepreneurship Figure 37 Entrepreneurship Goals Among Start-Ups 60% 50% % Percentage of Entrepreneurs 30% 20% 10% 0% Economic Goals Social Goals Economic and Social Goals Not-for-Profit Source: GEM United States 2008 and 2009 Adult Population Surveys (APS) In this sample, the total number of start-ups funded by more than one source are quite small (n=149 in 2008 and n=113 in 2009), and generalizations must be further restricted to the sample quality of the total Adult Population Survey. For example, the 149 responses here would have to be examined in light of the geographic distribution of the larger sample. In relation to the recession, the poor economy may explain the 24% drop in the number of startup ventures examined here that were funded by more than one source. Also of interest is the shift in emphasis from economic to social goals and the increase in not-for-profit ventures. Turning to ongoing ventures, separate from startups, we now examine Figure 38, which reports on 463 respondents in 2008 and 354 in

51 Social Entrepreneurship Figure 38 Entrepreneurship Goals Among Ongoing Ventures 70% % 50% Percentage of Entrepreneurs 40% 30% 20% 10% 0% Economic Goals Social Goals Economic and Social Goals Not-for-Profit Source: GEM United States 2008 and 2009 Adult Population Surveys (APS) Among established businesses we also see a drop in the number of reporting ventures of nearly 24%, suggesting that the poor economy may be similarly affecting new and ongoing ventures. The percentage of ongoing ventures motivated by economic goals remains higher than ventures with other goals, which may indicate that ventures focusing on financial performance rather than social mission are better able to endure the downturn. We now focus entirely on 2009 results. Who is pursuing which types of goals? Drawing from the 2009 sample beginning with start-ups receiving funding from multiple sources and ongoing concerns we can build a picture of some of the characteristics of the entrepreneurs involved. These characteristics begin with gender differences, as seen in Figure

52 Social Entrepreneurship Figure 39 Goals by Gender for Start-Ups 90% 80% Female Male 70% 60% Percentage of Entrepreneurs 50% 40% 30% 20% 10% 0% Economic Goals Social Goals Economic and Social Goals Not-for-Profit Source: GEM United States 2008 and 2009 Adult Population Surveys (APS) Among co-funded start-ups, male entrepreneurs outnumber their female counterparts by a factor of nearly three overall. The 6 to 1 ratio of male to female entrepreneurs in not-for-profits is striking, though the difference is less severe in ventures with social goals. We now turn to ongoing businesses in Figure

53 Social Entrepreneurship 80% Figure 40 Goals by Gender for Ongoing Businesses 70% Female Male 60% 50% Percentage of Entrepreneurs 40% 30% 20% 10% 0% Economic Goals Social Goals Economic and Social Goals Source: GEM United States 2008 and 2009 Adult Population Surveys (APS) Not-for-Profit 52 Among established businesses we see a far different picture, beginning with much less disparity between males and females and exact parity in ventures with not-for-profit goals. As a percentage, we see nearly double the percentage of women pursuing ventures with economic goals compared to start-up ventures with funding from more than one source, though the percentage of women concerned with social goals is about the same across the two sets. Among start-ups funded by more than one source, the most notable finding is that there are no such start-ups among age group, while that same cohort dominates all other age groups in start-ups with social goals, economic and social goals, and not-for-profit-goals. As a percentage of all age groups (n=115), this young cohort representing ventures with both economic and social goals is significantly higher than any other age group in any of the other goal categories. This may be evidence of a greater concern for social dimensions among the newest generation to engage in business, even in, or in spite of, the recessionary context. The picture of ongoing businesses is vastly different. Here we see a commanding presence of the age group in ventures with economic goals as well as those with both social and economic goals. However, both the numbers and percentage of the age group in ventures in all categories of goals are also significant and bear watching in future years as the baby boomer generation ages. As to values, respondents were asked to allocate 100 points across three categories according to the ability to generate economic, social and environmental value. Among a total of 256 start-ups, 168 out of 250 respondents to this variable (67%) were primarily focused on economic value, and of these, 31 (12%) were exclusively focused on economic value. Thirty-five start-ups out of 215 (16%) were primarily focused on social value, and of these, only 4 (2%) were exclusively focused on social value. Finally, 16 start-ups out of 169 (9%) were primarily focused on environmental value, and none were exclusively focused on environmental value. Turning to 338 ongoing firms, 260 out of 330 that responded to this variable (79%) were primarily focused on economic value, of which 47 (14%) were exclusively focused on economic value. Forty-one firms out of 276 (15%) were concerned primarily with social value, of which three (1%) were exclusively interested in social value. And 17 out of 202 (8%) were focused on environmental value, of which 3 (1%) were exclusively concerned with environmental value.

54 Social Entrepreneurship SOCIAL, ENVIRONMENTAL AND COMMUNITY OBJECTIVES This section will look specifically and in detail at ventures that have a particularly social, environmental or community objective. For convenience, we will call these socially-oriented ventures. The GEM question follows: Are you, alone or with others, currently trying to start or currently owning and managing any kind of activity, organization or initiative that has a particularly social, environmental or community objective? This might include providing services or training to socially deprived or disabled persons, using profits for socially oriented purposes, organizing self-help groups for community action, etc. This question was introduced in It will be interesting to compare it to 2010 data. Of 4,920 responses to this question, nearly 7% (n=330) answered affirmatively. Of these 330 respondents, most (62%, n=206) indicated that they were currently trying to start such a venture; 76 (23%) said they were currently owning or managing such a venture; and 48 (15%) reported that they were both trying to start and owned and managed such a venture. Let s look more closely at this group and their responses. Overall, women (52%, n=170) were in the slight majority of entrepreneurs pursuing these sociallyoriented ventures in Women (n=113, 55% of 206) were even more prevalent in the start-up role. Among age groups, these entrepreneurs were almost evenly divided in the groupings between 18 and 54 years old. Yet 14% were between 55 and 64, and 8% (28 out of 330) were 65 or older. Slightly over half indicated they were working fulltime; however, the data do not show whether they were primarily employed by their own firms or by another employer. About 26% had household incomes ranging from under $15,000 to $35,000. And 15% were in households earning from $150,000 to over $200,000 per year. Whether start-up or established entrepreneurs, slightly over half of the respondents (51%) had a bachelor s degree or higher in education. A disproportionate number were Caucasian (78%), and less than 8% were Black/African-American, while less than 2% were Hispanic, followed by even fewer Southeast Asian-Americans. Eighteen respondents (5%) were not born in the United States. Well over twice the number of these entrepreneurs The percentage of these entrepreneurs with great confidence in the nation s economic outlook (66%) is more than twice as high as the percentage who are less confident (28%), though fewer (51%) say they would be more likely to start a new project than in the past. Nevertheless, over 60% favor the outlook for new projects and 52% see good opportunities for starting a business in the next six months. A solid majority of 69% feels they have the required knowledge and skills to start a new business, but the 48% who show less confidence, as well as the 29% who express a fear of failure as a reason not to start a business, could represent an opportunity for business educators. These entrepreneurs feel that the population considers entrepreneurship as a good career (65%), that it confers high status (78%) and that it gets lots of media coverage (66%). The following analysis pertains to those respondents who are currently trying to start, or trying to start and owning/managing a socially-oriented business. The analysis does not include current ownersmanagers who are not currently trying to start such a venture. Of 137 respondents, 32 (23%) had either been providing services to others or had received external funding for more than three months. Thus, the majority of these ventures (77%, n=105) were less than three months old at the time of the survey. While more ventures had been started since 2008 (18) than in any prior year, the earliest start year was 1962, suggesting a very long ramp. Of 134 ventures, 92 (69%) had ten or fewer people, including volunteers but not counting the owners, at work, while 6 (4%) had 100 or more at work (2 ventures had 2,000 at work). Sixteen of 92 ventures reported using no volunteers, while the others reported a range of 1 to 100 volunteers, and two reported using 1,000 volunteers. Thirty-six (47%) of 76 ventures are using part-time workers. Of 100 respondents, 28 report that they will have up to ten workers, not counting owners, on the job when the venture is five years old, while 13 indicate that they will have 100 or more such workers. One current start-up indicates 50,000 people at work in five years. Of 140 respondents, 44% indicate that the ventures will be supported by sales of products or fees for services, and of 52 respondents, 46% plan to generate 100% of total income through such sales. In reference to generated value, 34% of 95 respondents plan to create economic value, with one indicating a 100% dedication to economic value; 65% of 138 respondents will generate societal value, with 22 (16%) dedicated to 100% societal value; and 21% of 81 ventures will generate environmental value, with none fully dedicated to such value. Of 138 responses, 39% report that they do not plan to measure generation of these values. 53

55 Social Entrepreneurship Thirty percent of 145 ventures were offering a new type of product or service; 33% of 144 ventures a new way of producing a product or service; 45% of 146 ventures a new way of delivering a product or service; 39% of 145 ventures a new way of promoting or marketing a product or service; and 49% of 144 ventures planned to address a new or so far unattended market niche or customer. Some 41% of 139 ventures believed that their offering would be served elsewhere in the market. Overall, among the start-up entrepreneurs, 69% agreed that their business is a success, and 88% of the ongoing owners-managers also agreed that their business is successful. CONCLUSION The foregoing discussion has examined the state of social entrepreneurship in relation to preferred business goals and priority values as well as those ventures that have a particularly social, environmental or community objective. Where it seemed relevant, we drew attention to the possible influence of the recession. While we make no claim as to who would self-select as a social entrepreneur, the data here do provide a bottom-up picture of concerns for other than purely economic motives. Greater evidence and clearer trends are expected to emerge in forthcoming reports. 54

56 5 The Impact of the Recession on the Entrepreneurial Composition of the United States by Region, Population Group and Minorities Edward Rogoff and Thomas S. Lyons The Recession has taken a terrible toll on entrepreneurship throughout the United States, a toll that has fallen disproportionately within certain regions and among specific minorities. Because business formation and growth is a long-term process, the effects of these casualties will be felt for years. The downturns in regional economies and among ethnic communities will produce widespread financial strains and increase demands for business support services that will likely exceed government and private organization capacities. Population and demographic patterns will be permanently reshaped by the recession, particularly among entrepreneurs. Table 3 presents the characteristics of the GEM 2008 and 2009 United States data sets. The prevalence rates for four types of entrepreneurs show a clear pattern of decline, especially between 2008 and Early-stage entrepreneurship dropped from 8.7% of the sample in 2008 to 6.9% in Established entrepreneurs who have been in business for 42 months or longer declined from 7.7% to 5.7%. Opportunity-driven entrepreneurs also declined from 7.2% to 4.8%. Only necessity-driven entrepreneurs saw an increase from 1.0% to 1.6%, perhaps reflecting a contraction in traditional employment options. As expected, this drop in entrepreneurial activity has not affected all regions and types of entrepreneurial activity in the United States equally. While overall there was a decline in the total amount of entrepreneurial activity from 28.3% in 2008 to 25.0% in 2009, it appears that certain key types of entrepreneurship, along with specific regions (the West and Midwest), showed fundamental and potentially deleterious shifts. The West reported the largest overall decline in total entrepreneurial activity, from 34.7% in 2008 to 27.3% in 2009, but this drop seems to be concentrated among some economically important subsets of entrepreneurs. Table 3 United States Survey Demographics, Mean Age 48.6% 46.0% % Male 53.1% 48.6% % Female 46.9% 51.4% % Within the Following Ethnic Groups White/Caucasian 79.8% 80.0% Non-White/Caucasian 20.2% 20.0% % Immigrants 7.5% 7.0% % Educational Attainment None 2.7% 3.0% Some Secondary 8.6% 10.1% Secondary Degree 22.8% 24.2% Post-Secondary 24.9% 21.3% University Bachelors Degree or Higher 41.0% 41.3% % Geography U.S. Census Bureau Regions Northeast 19.9% 18.5% Midwest 22.7% 22.1% South 35.9% 36.4% West 21.5% 23.3% % Early-Stage Entrepreneurs 8.7% 6.9% % Established Entrepreneurs 7.7% 5.7% % Opportunity Entrepreneurs 7.2% 4.8% % Necessity Entrepreneurs 1.0% 1.6% Source: GEM United States 2008 and 2009 Adult Population Surveys (APS) In the West region, total early-stage entrepreneurs dropped from 11.1% in 2008 to 7.6% in 2009, foreshadowing greater decreases in the overall level of entrepreneurship. A subset of these early-stage entrepreneurs are people managing businesses that are less than 42 months old. This group declined from 6.5% in 2008 to 3.2% in Similarly, opportunitydriven entrepreneurs in the West fell from 9.7% in 2008 to 5.0% in This trend is reversed in the case of necessity entrepreneurship often motivated by poor employment prospects, small growth plans and higher failure rates which increased in 2009 to 1.7% from 0.9% the year before. The Midwest revealed a somewhat similar pattern. The percentage of people involved in any form of entrepreneurial activity dropped modestly from 23.7% in 2008 to 21.2% in Contrast that with total early-stage entrepreneurship, which dropped from 7.4% to 4.8%, and the owner-managers of businesses less than 42 months old, which fell from 3.2% to 1.4%. Necessity-driven entrepreneurship rose from 0.2% to 1.5%. 55

57 The Impact Of The Recession On The Entrepreneurial Composition Of The United States By Region, Population Group And Minorities Exhibit 1 Early-Stage Entrepreneurship Among Ethnicities TEA Rate Ethnicity American Indian 12.5% 5.8% White/Caucasian 8.4% 6.5% Black/African-American 13.3% 7.0% Hispanic/Latino (non-mexican) 8.6% 10.7% Mexican 12.5% 15.0% Asian 7.9% 8.8% Other 8.7% 7.7% Source: GEM United States 2008 and 2009 Adult Population Surveys (APS) The patterns in the West and Midwest are to lesser extents reflected in other regions, as well as the country as a whole. In the Northeast, it is worth noting that early-stage entrepreneurial activity declined from 8.0% in 2008 to 6.4% in 2009, ownermanagers of businesses less than 42 months old dropped from 3.7% to 1.8%, while necessity-driven entrepreneurship was on the rise. Across the United States, this represents a clear picture of declining numbers of new, high-growth potential ventures and an increase in smaller necessity-driven ventures, which are more likely to fail. Table 4 Measures of Entrepreneurial Activity United States Census Regions Year Northeast Midwest South West United States Involved in Any Kind of Entrepreneurial Activity Involved in Total Early-Stage Entrepreneurial Activity Involved in Opportunity Early-Stage Entrepreneurial Activity Involved in Necessity Early-Stage Entrepreneurial Activity Actively Involved in Start-Up Effort; Owner, No Wages Yet Manages and Owns a Business That Is Up to 42 Months Old Manages and Owns a Business That Is Older Than 42 Months % 23.7% 28.3% 34.7% 28.3% % 21.2% 25.9% 27.3% 25.0% % 7.4% 8.6% 11.1% 8.7% % 4.8% 8.1% 7.6% 6.9% % 6.8% 6.4% 9.7% 7.3% % 2.7% 5.9% 5.0% 4.8% %.2% 1.4%.9% 1.0% % 1.5% 1.7% 1.7% 1.6% % 4.3% 5.4% 4.7% 4.8% % 3.4% 4.3% 4.6% 4.2% % 3.2% 3.2% 6.5% 4.0% % 1.4% 3.7% 3.2% 2.7% % 7.1% 7.5% 8.8% 7.8% % 5.1% 5.7% 6.6% 5.7% Shut Down a Business in the % 1.8% 3.1% 2.7% 2.5% Past 12 Months % 1.7% 2.6% 3.0% 2.2% Source: GEM United States 2008 and 2009 Adult Population Surveys (APS) 56

58 The Impact Of The Recession On The Entrepreneurial Composition Of The United States By Region, Population Group And Minorities Table 4 breaks out the data into Caucasian and non-caucasian subgroups. Exhibit 1 shows the sample breakout by ethnic group. The patterns in Table 5 comparing the United States as a whole to its regions are somewhat more pronounced among non-caucasians, especially in the economically hard-hit Midwest. Because the non-caucasian samples, particularly when broken out by region, are small, one must be careful about over-interpreting results. But clearly, early-stage entrepreneurial activity among non-caucasians in the Midwest has dropped considerably, by perhaps as much as half. For example, whereas total early-stage, opportunitydriven entrepreneurship among non-caucasians was 17.9% in 2008, it fell to 5.3% in Among the same sample, involvement in a start-up that was not yet earning wages declined from 14.6% in 2008 to 7.4% in Although less pronounced, the trend across the United States is generally similar. The one bright spot in this otherwise dismal scenario is a drop in the percentage of non-caucasian entrepreneurs who closed their businesses in 2009 as compared to Nationwide, 2.9% reported shuttering their establishments in 2008, while only 1.4% reported closing their ventures in Even in the Midwest, there was a decrease in reported business closures among non-caucasians, which fell from 1.6% in 2008 to 0% in Table 5 Measures of Entrepreneurial Activity by White/Caucasian and Non-White/Caucasian Involved in Any Kind of Entrepreneurial Activity Involved in Total Early- Stage Entrepreneurial Activity Involved in Opportunity Early-Stage Entrepreneurial Activity Involved in Necessity Early-Stage Entrepreneurial Activity Actively Involved in Start-Up Effort; Owner, No Wages Yet Manages and Owns a Business That is Up to 42 Months Old Manages and Owns a Business That is Older Than 42 Months Shut Down a Business in the Past 12 Months Year Caucasian Northeast Midwest South West United States Non- Caucasian Caucasian Non- Caucasian Caucasian Non- Caucasian Caucasian Non- Caucasian Caucasian Non- Caucasian % 35.8% 20.7% 43.1% 27.9% 32.2% 34.8% 35.6% 27.1% 35.3% % 29.2% 21.3% 18.1% 25.4% 28.9% 25.6% 31.0% 24.2% 28.3% % 9.3% 6.0% 18.7% 8.7% 9.4% 11.3% 9.6% 8.4% 11.0% % 9.0% 4.6% 7.4% 8.2% 7.9% 6.9% 8.7% 6.5% 8.2% % 6.5% 5.3% 17.9% 6.6% 6.5% 10.1% 7.2% 7.0% 8.5% % 6.1% 2.5% 5.3% 6.1% 5.1% 4.5% 7.1% 4.5% 5.9% % 2.8%.2% 0% 1.6%.6%.6% 2.4%.9% 1.3% % 3.0% 1.7% 0% 1.7% 2.0% 2.0% 1.2% 1.6% 1.6% % 7.5% 2.9% 14.6% 5.2% 6.7% 4.4% 4.8% 4.3% 7.6% % 9.0% 3.1% 7.4% 4.1% 5.9% 4.3% 6.0% 3.8% 6.5% % 1.9% 3.1% 4.1% 3.5% 2.3% 6.9% 5.3% 4.2% 3.3% % 0% 1.6% 0% 4.2% 2.2% 2.7% 4.0% 2.8% 2.2% % 9.3% 7.1% 5.7% 8.4% 4.4% 8.8% 10.0% 8.1% 6.8% % 2.0% 5.5% 0% 6.6% 2.3% 7.8% 2.8% 6.4% 2.1% %.9% 1.8% 1.6% 3.0% 3.2% 2.2% 3.8% 2.4% 2.9% % 0% 1.9% 0% 2.8% 1.1% 3.1% 2.8% 2.4% 1.4% Source: GEM United States 2008 and 2009 Adult Population Surveys (APS) The pattern of entrepreneurship by gender reveals very similar changes over the last year among both men and women, as shown in Table 6. Generally, the level of entrepreneurial activity in the GEM U.S. sample is lower among women than men, but when examined uniformly by region and type of entrepreneurial activity, there are significant and similar declines among women and men. This uniform pattern of change holds when the differences between men and women are broken out by region. 57

59 The Impact Of The Recession On The Entrepreneurial Composition Of The United States By Region, Population Group And Minorities Table 6 Entrepreneurial Activity by Gender Northeast Midwest South West United States Year Male Female Male Female Male Female Male Female Male Female Involved in Any Kind of Entrepreneurial Activity Involved in Total Early- Stage Entrepreneurial Activity Involved in Opportunity Early-Stage Entrepreneurial Activity Involved in Necessity Early-Stage Entrepreneurial Activity Actively Involved in Start-Up Effort; Owner, No Wages Yet Manages and Owns a Business That is Up to 42 Months Old Manages and Owns a Business that Is Older Than 42 Months Shut Down a Business in the Past 12 Months % 21.8% 28.9% 17.8% 30.3% 26.0% 39.4% 29.0% 32.3% 24.0% % 20.6% 24.3% 18.2% 31.1% 21.1% 33.4% 21.4% 29.8% 20.4% % 5.4% 9.5% 4.9% 7.6% 9.8% 13.1% 8.8% 9.8% 7.6% % 4.3% 5.1% 4.4% 11.0% 5.4% 9.5% 5.7% 8.8% 5.0% % 4.3% 9.0% 4.3% 7.4% 5.3% 12.0% 6.9% 9.1% 5.2% % 3.2% 2.1% 3.4% 8.4% 3.6% 5.6% 4.6% 6.0% 3.7% % 1.1% 0.2% 0.2% 0.2% 2.8% 0.5% 1.5% 0.4% 1.6% % 0.9% 2.1% 1.1% 2.3% 1.1% 2.8% 0.7% 2.3% 0.9% % 3.2% 5.5% 2.9% 4.6% 6.2% 6.3% 2.9% 5.4% 4.2% % 3.2% 3.8% 3.0% 6.2% 2.7% 6.0% 3.3% 5.6% 3.0% % 2.4% 4.2% 2.0% 2.9% 3.6% 7.0% 6.1% 4.5% 3.5% % 1.3% 1.1% 1.4% 4.8% 2.7% 4.2% 2.4% 3.5% 2.1% % 6.0% 8.7% 5.3% 9.4% 5.4% 9.8% 7.7% 9.3% 6.0% % 3.7% 6.2% 4.1% 6.7% 4.7% 7.9% 5.5% 6.9% 4.6% % 1.3% 2.0% 1.4% 3.3% 2.9% 3.0% 2.3% 2.8% 2.1% % 0.6% 1.7% 1.6% 2.8% 2.3% 3.7% 2.4% 2.5% 1.9% Source: GEM United States Adult Population Surveys (APS) Attitudes among entrepreneurs provide revealing and substantiating detail to these patterns as shown in Table 7. In 2009, U.S. entrepreneurs uniformly rated the climate as negative, with few business opportunities. This pessimism is relatively uniform among early-stage and established entrepreneurs as well as across regions, but it seems greatest in the Midwest where only 11.1% of business owners employing fewer than five employees and none of the sample of those employing six or more people expected to engage in another start-up in the next three years. 58

60 The Impact Of The Recession On The Entrepreneurial Composition Of The United States By Region, Population Group And Minorities Table 7 Measures of Entrepreneurship Attitudes Based on Size of Firm Northeast Midwest South West United States TEA Total Early-Stage Entrepreneurs EB- Established Entrepreneurs Current Jobs Current Jobs Current Jobs Current Jobs Current Jobs Year TEA: Expects to Start-Up in the Next 3 Years: % Yes % 0.0% 11.1% 0.0% 25.8% 100.0% 21.2% 25.0% 21.7% 60.0% EB: Expects to Start-Up in the Next 3 Years: % Yes % 22.2% 14.9% 42.9% 7.2% 3.4% 13.0% 17.6% 11.2% 14.5% TEA: Believes Starting a Business Is Considered a Good Career Choice: % Yes EB: Believes Starting a Business Is Considered a Good Career Choice: % Yes TEA: Starting a Business Now Compared to One Year Ago: % More Difficult TEA: Growing a Business Now Compared to One Year Ago: % More Difficult TEA: Effect Global Economic Crisis: %Fewer Business Opportunities EB: Starting a Business Now Compared to One Year Ago: % More Difficult EB: Growing a Business Now Compared to One Year Ago: % More Difficult EB: Affect Global Economic Crisis: % Fewer Business Opportunities % 100.0% 60.0% NA 71.8% NA 76.5% 100.0% 64.9% 100.0% % 66.7% 56.3% 100.0% 70.3% 72.7% 62.1% 33.0% 65.3% 61.5% % 100.0% 50.0% 0.0% 58.4% 20.0% 64.3% 100.0% 58.2% 55.6% % 0.0% 50.0% 100.0% 48.5% 0.0% 36.3% 33.3% 40.6% 20.0% % 0.0% 43.8% 100.0% 63.6% 60.0% 45.5% 66.7% 56.2% 66.6% % 85.7% 78.3% 57.1% 73.9% 82.8% 75.5% 78.9% 75.6% 78.7% % 44.4% 64.6% 37.5% 49.3% 32.2% 56.3% 66.6% 55.3% 43.8% % 66.6% 74.5% 50.0% 67.2% 60.7% 67.3% 66.7% 68.0% 62.5% Source: GEM U.S. Adult Population Survey Source: GEM United States 2009 Adult Population Surveys (APS) 59

61 The Impact Of The Recession On The Entrepreneurial Composition Of The United States By Region, Population Group And Minorities Table 8 breaks out entrepreneurial activity into four industry groups: Extractive, such as mining and farming; Transforming, such as manufacturing; Business Services, such as consulting and telecommunications; and Consumer-Oriented, such as retail. Extractive and transforming industries show the least amount of change between 2008 and 2009, while business services companies show the largest declines. For example, in the United States total early-stage entrepreneurial activity in the business services sector declined from 38.3% in 2008 to 28.6% in Among established businesses, this drop was from 34.9% to 26.4%. Interestingly, entrepreneurial activity in the consumer-oriented sector actually increased for both early-stage, which jumped from 35.9% to 41.1%, and established businesses, which went from 32.8% to 34.3%. One can only speculate as to the reasons that this would happen in a declining economy when consumer spending dropped by historic amounts. Perhaps the low capital and technical training requirements of these businesses relative to other sectors allowed these entrepreneurs to both start and continue their businesses during a recession. It may also be that other barriers to entry in the consumer-oriented sector are perceived as being lower. However, one must be quite skeptical about the survival prospects of these companies if consumer spending does not rebound. Table 8 Industry Distribution by Early-Stage and Established Business Owner Entrepreneurs Northeast Midwest South West United States Early- Stage Established Business Owner Early- Stage Established Business Owner Early- Stage Established Business Owner Early- Stage Established Business Owner Early- Stage Established Business Owner Year % within Region % within Region % within Region % within Region % within Region % within Region % within Region % within Region % within Region % within Region Extractive Transforming Business Services Consumer- Oriented % 5.3% 1.4% 21.1% 3.3% 10.6% 6.1% 11.2% 3.7% 11.8% % 12.8% 5.8% 23.6% 7.0% 5.0% 7.1% 13.0% 6.0% 12.1% % 22.7% 23.6% 19.7% 17.3% 19.7% 28.9% 20.2% 22.2% 20.4% % 19.1% 21.2% 20.0% 22.5% 28.7% 30.6% 35.1% 24.4% 27.1% % 36.0% 41.7% 31.6% 39.3% 34.8% 37.7% 37.1% 38.3% 34.9% % 27.7% 19.2% 25.5% 31.7% 29.7% 29.4% 22.1% 28.6% 26.4% % 36.0% 33.3% 27.6% 40.0% 34.8% 7.2% 31.5% 35.9% 32.8% % 40.4% 53.8% 30.9% 38.7% 36.6% 32.9% 29.9% 41.1% 34.3% Source: GEM U.S. Adult Population Survey Source: GEM United States 2008 and 2009 Adult Population Surveys (APS) Conventional wisdom holds that more people start their own businesses when the economy is down. While the percentage of necessity-driven entrepreneurship has indeed risen during this recession, overall entrepreneurial activity is in decline relative to several key variables. Early-stage and established entrepreneurship both decreased significantly between 2008 and Opportunitydriven entrepreneurship, which spurs both job and wealth creation, also declined steeply during this time period. Non-Caucasian entrepreneurs have suffered disproportionately. None of this bodes well for the U.S. economy, which needs entrepreneurial activity to stimulate growth and development. 60

62 6 Financing William D. Bygrave No doubt about it, financing for small businesses was scarce in Bank lending decreased, credit card borrowing was more difficult, trade credit tightened, informal investment dropped and venture capital investment plummeted. SMALL BUSINESS LENDING Despite receiving $700 billion of bailout money, most banks reduced their lending to small business by 9% in 2009 according to Elizabeth Warren, the congressional TARP watchdog. x Even when banks did lend them money, small businesses were charged an average of 3.5 percent more than the federal funds rate on small commercial and industrial loans the biggest spread since the Federal Reserve started reporting the data in xi The situation alarmed Washington policy makers because, in normal times, small businesses create far more jobs than mid-sized and big businesses, but as the United States struggled to pull out of the recession, small business hiring was slowing down in For example, Intuit, which processes payrolls for small businesses, reported that although small businesses hired 60,000 additional workers in February 2010, that number declined to 40,000 in April, to 32,000 in May and to only 16,000 in June. According to another payroll company, Automatic Data Processing Inc., job creation by small businesses stalled completely in June when they actually lost 1,000 jobs. xii In response to pressure from the White House at the end of 2009, a number of major banks announced plans to increase small business loans in Wells Fargo intended to increase lending 25% compared to JPMorgan Chase said it would extend $10 billion in new credit to small business in 2010 an increase of $4 billion over Bank of America announced it would loan $5 billion more in 2010 than in And in February 2010, Huntington National Bank, a regional bank based in Columbus, Ohio, serving the Midwest, set a goal to increase small business lending by $1.2 billion in xiii In a speech, Restoring the Flow of Credit to Small Business, on July 12, 2010, Ben Bernanke, Chairman of the Federal Reserve, said, Making credit accessible to sound small businesses is crucial for our economic recovery, and so should be front and center among our policy changes. xiv Bernanke said that credit conditions for small business had tightened in the last three months and were at very elevated levels historically. He also reported that one measure of banks small business loans dropped from more than $710 billion in the second quarter of 2007 to less than $670 billion in the first quarter on xv He pointed out three major factors that were contributing to the downturn: weaker demand for small business loans, deterioration in the financial conditions of small businesses during the recession and restricted availability of credit. Bernanke stressed that to support the economic recovery, ways had to be found to ensure that creditworthy borrowers had access to needed loans. Banks appear to be responding to Washington s exhortations and are stepping up their efforts to lend more money to creditworthy small businesses. But it remains to be seen if banks small business lending (including credit cards) will increase overall in And if it does, will it be enough to boost the economic recovery? INFORMAL INVESTING Every new venture, from mom-and-pop convenience stores to Silicon Valley superstars such as Google, starts with an investment from the founders themselves or the so-called 3Fs (Family, Friend or Foolhardy strangers). Those informal investors are vital to the start-up process; if all of them stopped providing money to start-ups, the U.S. economy would immediately feel the effect with a sudden jump in unemployment. What s more, informal investments flow almost instantaneously into the economy when entrepreneurs spend their investments to buy goods and services for their new ventures; thereby informal investment supports many more secondary jobs (the multiplier effect). Informal investors take money from their savings and current income when they invest in entrepreneurs. So when stock markets crashed in the aftermath of the banking industry meltdown in September/October 2008, it was likely that informal investors, especially ones whose net worth fell sharply, became wary about putting money into risky entrepreneurial ventures. The GEM 2008 surveys were conducted in the spring and early summer, so they preceded the banking industry meltdown; this meant that we had to wait until the GEM survey in the spring and summer of 2009 to see how the banking industry meltdown affected informal investing. Because informal investment is relatively rare in most countries, the GEM measure of informal investment asks respondents if they have invested in someone else s new business in the past three years. It is therefore a smoothed measure; it is not a measure of activity in just one year. Thus it is not possible to give precise numbers for the amount of informal investment in a given year, but it is possible to infer year-to-year changes and its approximate magnitude. As it turned out in 2009, the United States informal investor prevalence rate was the third lowest since 1999 when 61

63 Financing GEM began its annual surveys, and the average amount invested dropped noticeably; thus there was substantially less informal investment in the aftermath of the meltdown. VENTURE CAPITAL Venture capital had a very poor year in xvi The amount invested in portfolio companies was down 37% compared with 2008 with the number of deals down 30%, and commitments of new capital was 46% less than in The U.S. venture capital industry was severely affected by the recession, but according to National Venture Capital Association, During 2009, the industry continued the downsizing that became very visible in mid While the global economic issues that surfaced in 2008 accelerated the decline and definitely added stresses to the industry, this resizing is a function of the technology bubble bursting several years earlier. xvii Those two factors, the recession and the aftermath of the bursting of the technology bubble, combined with other factors to produce the weakest market for initial public offerings (IPOs) of venture-backed companies in both 2008 and 2009 since at least The IPO drought severely depressed the returns on venture capital, which made venture capitalists gloomy and less inclined to invest in portfolio companies and institutional investors wary about making new commitments of money to venture capital funds. Approximately 3,000 of the 27,000 or so companies (about 11 percent) financed with venture capital between 1970 and 2004 have had IPOs. xviii Of the others that were harvested, mergers and acquisitions were the most common exit. In comparatively rare instances, the company s managers bought back the venture capitalist s investment. The highest return on a venture capital investment is produced when a company has an initial public offering (IPO) or is merged with or acquired by another company (M&A) for a substantial capital gain. In general, however, M&As do not produce nearly as big a capital gain as IPOs because most M&As are venture-backed companies that aren t successful enough to have an IPO. For instance, one way of harvesting struggling portfolio companies is to sell them to other companies for a modest capital gain, or in some cases a loss. The average post-ipo valuation of venture capital-backed companies that went public in the five years through 2008 was $523 million, xix compared with an average valuation of $122 million for those that were exited through mergers and acquisitions. xx The average IRR to limited partners of classic venture capital funds has been in the mid-teens over the entire period since the first fund was formed in But during those six decades there have been periods when the returns have been higher and/or lower. When the IPO market is booming, the returns on venture capital are high, and vice versa. The oneyear returns for 2008 were awful because the IPO market was dismal with only 6 IPOs in the entire 12 months compared with 86 in Indeed, the IPO market was so dreadful in 2008 and the first half of 2009 that no venture capital-backed companies went public in the second quarter of 2008 or the first quarter of 2009; the last time that happened was more than 30 years ago! The importance of the IPO market for venture capital is demonstrated in Figure 41; it shows a close correlation between the total amount raised by venture-backed IPOs and the return (IRR) on venture capital funds when the IPO market was thriving, as it was in 1999 and early 2000, the returns on venture capital were high and vice versa. Not only do lucrative IPOs directly produce spectacular returns on venture capital invested in the companies going public, but they also raise the returns on mergers and acquisitions, because IPO market valuations tend to set the valuations of all private equity deals. For instance, in 2000 during the Internet bubble, the average valuation for a venture capital backed M&A was $338.4 million, but by 2002, following the burst of the bubble, the average valuation fell to $52.2 million. 62

64 Financing Figure 41 Venture Capital IRR and Total Amount Raised by VC-Backed IPOs Total Raised by VC-Backed IPOs, $ Millions 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2, Venture Capital IRR, % % -22% Amount of IPOs CPI Adjusted VC IRR % Source: National Venture Capital Association Beginning in the second quarter of 2003, the number of venture-capital-backed IPOs and the amount raised in the offerings in the United States began an upward trend that built substantial momentum in 2004, when 83 venture-backed companies had IPOs. Google s spectacular IPO in the third quarter of 2004 boosted the confidence of the venture capital industry. Some industry leaders expected that it would herald the start of a new cycle in venture capital investing, with more money being invested in seed, start-up, and early-stage businesses. What they were hoping for was another revolutionary innovation that would fire up the enthusiasm of investors, such as personal computers did at the beginning of the 1980s and the Internet and Web did in the mid-1990s. The hoped-for boom in nanotechnology IPOs never materialized, and now in 2010 they are betting on clean technology. Clean technology is attracting a lot of venture capital, with the amount invested increasing ten-fold from $0.4 billion in 2004 to $4.1 billion in 2008; however, investment in clean technology fell in 2009 along with venture capital investment in general (See Figure 42). Silicon Valley s preeminent venture capital firm, Kleiner Perkins Caufield & Byers, recruited Al Gore as a partner at the end of According to Gore, who was cited for for informing the world of the dangers posed by climate change when awarded the 2007 Nobel Peace Prize, clean technology will be bigger than the Industrial Revolution and significantly faster. xxi But an IPO boom in clean technology stocks has yet to materialize. Investors are still shy of the IPO market after the financial beating they took when the Internet IPO bubble burst. Most of them had negative returns on their stock portfolios over the last ten years with returns on the NASDAQ of -3.2% per year and the S&P 500 of -3.0%. 63

65 Financing Figure 42 Venture Capital Investments in Clean-Tech in United States $1,000, Data source: National Venture Capital Association Yearbook, 2010 At the time this report is being written in mid-2010, the outlook for venture capital is looking brighter. The amount invested in portfolio companies in the first six months of 2010 increased 49% over the same period of 2009, and clean technology investment is on track to hit a new high. But the amount raised by venture capital funds fell, with a 42% decline in commitments. IPO momentum is building, with nine venture-baked IPOs in the first quarter followed by 17 in the second quarter. However, while the current trend in venture-backed IPOs is encouraging, it is far below the level needed to sustain the venture capital industry long term. xxii 64

66 7 Public Policy In The United States By Julian Lange, Joseph Onochie and Ivory Phinisee Previous GEM reports have suggested that in high-income countries, public policy should focus on maintaining entrepreneurial competitiveness and sustaining innovation rates. Of equal importance is the availability of sufficient early-stage funding. Tables and figures from last year s GEM United States Report are updated to show the changes that occurred in 2009 as a result of the recession, which began in the latter part of Following are some highlights: The 2008 decline in the availability of sufficient funding for entrepreneurs from key funding sources continued in 2009 (Figure 45), according to the assessment of GEM national experts in the United States. Eight industries showed declines in growth rates in 2008, with the largest percentage of losses occurring in the construction and wholesale trades (Table 9). In 2009, the largest percentage losses were incurred in the mining industry, followed closely by construction and manufacturing, respectively. In 2008, the gender gap decreased between female and male prevalence rates. In 2009, both male and female prevalence rates declined, with the gap remaining essentially unchanged. In 2008 the GEM national experts perceptions of good opportunities to create new firms both now and in the last five years declined (Figure 44). However, for 2009, on average, the perceptions of GEM national experts concerning the existence of good opportunities to create new firms now increased markedly. The 2008 data showed a dramatic reduction in the dynamism levels in the United States, continuing the trend of the previous two years; however the drop was explained by a change in the GEM survey methodology, which resulted in a significant upward adjustment to the established business rate (Table 11). In 2009, the U.S. dynamism rate experienced a slight up-tick from the 2008 value, but there were significant declines in the U.S. early-stage and established business ownership rates for IMPACT OF ECONOMIC DECLINES ON ECONOMIC ACTIVITY GEM data provide some evidence of the impact on U.S. entrepreneurial activity caused by the previous recession and the current economic slowdown. A decline in entrepreneurial activity appears to be attributed to: The meltdown in both the financial intermediation industry and in the capital markets triggered principally by the implosion of the financial market for subprime loans and their derivatives. The decline in the housing market as problems in the financial markets have created a drought in mortgage loan facilities even for high-credit borrowers. The increased prices of oil and other commodities. The Business Cycle Dating Committee of the National Bureau of Economic Research (NBER) determined that a peak in economic activity occurred in the United States in December The peak marks the end of the expansion that began in November It also marks the beginning of the recession. Pending the announcement by the NBER of the official date marking the end of the 2007 recession, GEM uses July 2009 (the third quarter) as an estimate. A recession is defined as a significant decline in economic activity across the economy, lasting more than a few months, normally visible in production, employment, real income and other indicators. A recession begins when an economy reaches a peak of activity and ends when the economy reaches a trough. Between trough and peak, the economy is in an expansion. Because a recession is a broad contraction of the economy, not confined to one sector, the Committee emphasizes economy-wide measures of economic activity, believing that domestic production and employment are the primary conceptual measures of economic activity. 5 5 The NBER does not define a recession in terms of two consecutive quarters of decline in real GDP. Rather, a recession is a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production and wholesale-retail sales. For more information, see the announcement from the NBER s Business Cycle Dating Committee, dated 12/01/08. 65

67 Public Policy in the United States Figure 43A shows changes in early-stage entrepreneurial activity alongside changes in real GDP and changes in the number of employees in the United States. As is evident in Figure 43A, declines in entrepreneurial activity occurred alongside declines in both real GDP and number of employees in the period surrounding the last recession of Again, we can observe declines in real GDP and entrepreneurial activity from 2006 to However, in 2008, early-stage entrepreneurial activity shows an increase over the 2007 activity as reported by GEM. The bias suspected in prior years earlystage prevalence rates was addressed in 2008 by changing the GEM survey methodology. Allowing for the upward adjustment of the prevalence rate, the differences between the 2008 and 2007 results are not significant. In contrast, 2009 early-stage entrepreneurial activity, change in real GDP and changes in the number of nonfarm employees all showed significant declines. In 2006, the U.S. housing market started its decline, causing early-stage job losses in construction and other industries associated with the housing market. This housing market decline may explain, in part, the drop in the early-stage prevalence rates in the United States in 2006 and in 2007 as well as the declining real GDP in The recession occurred starting in December 2007 in the United States due to, in large degree, the continuing decline in the housing market. Figure 43A U.S. Entrepreneurial Trends with Real GDP 12.0 % 10.0 % Percentage Entrepreneurial Activity 8.0 % 6.0 % 4.0 % 2.0 % Early-Stage Entrepreneurial Activity (18-99 pop.) Change in Real GDP Changes in #Nonfarm Employees 0.0 % -2.0 % -4.0 % Years Sources: 1) Early Stage GEM; 2) Real GDP Bureau of Economic Analysis 3) #Nonfarm employees Bureau of Labor Statistics 66

68 Public Policy in the United States Figure 43B tracks percent changes in U.S. real GDP and key economic components of the U.S. economy over time. Residential investment started to decline in 2006 and accelerated in 2007 and This decline in residential investment reflects the burst of the housing bubble. Growth of private domestic investment in manufacturing and other industries also declined starting in 2007, continuing in 2008 and Figure 43B Percent Changes: U.S. Real GDP and Key Components Percent Changes: U.S. Real GDP and Key Components %Change Year over Year Gross Domestic Product Personal Consumption Expenditures Services Gross Private Domestic Investment Investment Nonresidential Investment Residential Exports Imports Source: U.S. Bureau of Economic Analysis Gross domestic product Personal consumption expenditures Services Gross private domestic investment Investment Nonresidential Investment Residential Exports Imports 67

69 Public Policy in the United States Table 9 shows a breakdown of changes in U.S. growth rates by the industry categories used by the U.S. Bureau of Economic Analysis. In the recession year of 2001, the largest declines occurred in the agriculture and related industries, in the manufacturing industry and in the wholesale trade industry. In 2002, more industries experienced declines than in 2001 (five versus eight industries), with the largest declines occurring in the information industry, the mining industry and the manufacturing industry. Finally, Table 9 shows a decline in seven industries; more industries showed declining growth in 2007 than in In 2008, the largest percentage of losses occurred in the construction and wholesale trade industries. The manufacturing, agricultural, retail trade, information, financial activities and professional and business services industries also experienced declines in In 2009, the largest percentage losses were incurred in the mining industry, followed closely by the construction and manufacturing industries. Table 9 U.S. Percentage Growth Rates by Industry Industry All Agriculture and Related Industries Nonagricultural Industries: Mining Industry Construction Industry Manufacturing Industry Wholesale Trade Industry Retail Trade Industry Transportation and Utilities Industry Information Industry Financial Activities Industry Professional and Business Services Education and Health Services Industry Leisure and Hospitality Industry Other Services Industry Public Administration Industry Source: United States Bureau of Labor Statistics Employment Levels by Industry. U.S. recession occurred from March November 2001 U.S. recession began in December Until the official date is announced, July 2009 (the third quarter) is used as an estimate for the end of the recession (Source: Minneapolis Federal Reserve Bank). 68

70 Public Policy in the United States Table 10 shows changes in U.S. employment, business establishments and firms for the last two recession periods from July 1990 to March 1991 and from March 2001 to November There are substantial declines in growth rates of number of employees, business establishments and firms during these periods of economic slowdown. The table was updated with 2006 data the latest available. In 2006, we see that the growth of firms was flat at This was lower than during the prior recession, March 2001 to November Figure 44 and Figure 45 graph the data obtained from the GEM U.S. National Expert Survey (NES) on issues affecting entrepreneurial activity in the United States for the years 2006 to The tops of the bar graphs that start on the x-axis represent the mean responses of the experts. The length of the line extending equal distances below and above the tops of the bar graphs in both Figures 44 and 45 represent 1 standard deviation above the mean responses and 1 standard deviation below the mean responses of each bar graph. Figure 44 shows that, in 2008, national Table 10 Change in U.S. Employment, Business Establishments and Firms Year Employment % Change Establishments % Change Firms % Change Employment/ Establishments ,844,303 6,016,367 4,954, ,626, ,106, ,021, ,469, ,175, ,073, ,307, ,200, ,051, ,825, ,319, ,095, ,773, ,401, ,193, ,721, ,509, ,276, ,314, ,612, ,369, ,187, ,738, ,478, ,299, ,894, ,541, ,117, ,941, ,579, ,705, ,008, ,607, ,064, ,070, ,652, ,061, ,095, ,657, ,400, ,200, ,697, ,398, ,254, ,767, ,074, ,387, ,885, ,317, ,499, ,983, ,917, ,601, ,022, ,604, ,705, n.a. n.a U.S. Census Bureau Statistics of U.S. Businesses. These data were developed in cooperation with, and partially funded by, the Office of Advocacy of the U.S. Small Business Administration (SBA). Statistics of U.S. Businesses (SUSB) is an annual series that provides national and subnational data on the distribution of economic data by size and industry. Statistics of U.S. Businesses covers most of the country's economic activity. The series excludes data on nonemployer businesses, private households, railroads, agricultural production, and most government entities 69

71 Public Policy in the United States experts perceived, on average, the amount of good opportunities to create new firms was in decline both currently and in the last five years. However, in 2009 the average perception of national experts was much different. They perceived a markedly increased number of good opportunities to create new firms. Figure 45 shows the mean responses for GEM national experts regarding available funding from key funding sources for entrepreneurs in the United States. In 2007, GEM national experts responded to the statement that sufficient funding was available for entrepreneurs as Neither true nor false or Somewhat true. However, in 2008, GEM national experts mean responses regarding available funding were more pessimistic and declined to Neither true nor false. In 2009, GEM national experts mean responses for four of the six funding type categories further declined below Neither true nor false, with only the government subsidies category increasing slightly. The Private Individuals category remained unchanged. These declines in the experts opinions are indicative of the impact of the recession in the United States economy that began in December Figure 44 GEM U.S. National Expert Survey Mean Response for New Firm Entrepreneurship Opportunity 1 = Completely False 2 = Somewhat False 3 = Neither True nor False 4 = Somewhat True 5 = Completely True Good Opportunities to Create New Firms Now Good Opportunities to Create New Firms in Last 5 Years Source: GEM United States National Expert Surveys (NES) 70

72 Public Policy in the United States Figure 45 GEM U.S. National Expert Survey Mean Response for Available Funding 1 = Completely False 2 = Somewhat False 3 = Neither True nor False 4 = Somewhat True 5 = Completely True Equity Debt Government Subsidies Private Individuals Venture Capital IPOs Source: GEM United States National Expert Surveys (NES) Table 11 shows U.S. dynamism. GEM defines dynamism as the ratio of early-stage entrepreneurship to business ownership. High levels of dynamism are positively associated with high early-stage entrepreneurship prevalence rates, high venture capital investment and significantly higher levels of high expectation entrepreneurship. As Table 11 shows, there were substantial reductions in the dynamism levels in the United States in 2006 and 2007 compared to In 2008, there was also a dramatic drop, but this drop was due to a change in the survey methodology for the most part; we see a substantial upside adjustment to the established business rate due to a change in the methodology in In 2009, the U.S. dynamism rate experienced a slight up-tick from its 2008 value, but significant declines were observed in the 2009 U.S. earlystage rate and the 2009 U.S. established business ownership rate as compared to their values in Table 11 U.S. Dynamism Year U.S. Dynamism U.S. Early-Stage Rate * 8.0 U.S. Established Business Ownership Rate * 5.9 *GEM Survey Methodology was changed in 2008 to correct for a possible bias. There may have been a downside bias in previous years, especially involving the established business owners. 71

73 GEM National Teams 2010 Team Argentina Institution Center for Entrepreneurship, IAE Business School Universidad Austral National Team Members Silvia Torres Carbonell Leticia Arcucci Juan Martin Rodriguez Financial Sponsors Center for Entrepreneurship, IAE Business School, Universidad Austral Banco Santander Rio APS Vendor MORI Argentina Belgium Bosnia and Herzegovina Brazil Vlerick Leuven Gent Management School Entrepreneurship Development Centre Tuzla (in partnership with University of Tuzla) IBQP Instituto Brasileiro da Qualidade e Produtividade Jan Lepoutre Hans Crijns Miguel Meuleman Olivier Tilleuil Bahrija Umihanić Rasim Tulumović Boris Ćurković Senad Fazlović Admir Nuković Esmir Spahić Sla ana Simić Mirela Arifović Simara Maria S. S. Greco Paulo Alberto Bastos Junior Joana Paula Machado Rodrigo G. M. Silvestre Carlos Artur Krüger Passos Júlio César Felix Subsecretaría de Desarrollo Económico, Ministerio de Desarrollo Económico Gobierno de la Ciudad de Buenos Aires Prosperar, Agencia Nacional de Desarrollo de Inversiones Policy Research Centre Entrepreneurship and International Entrepreneurship, Flemish Government Municipality of Tuzla Government of Tuzla Canton Foundation of Tuzla Community Instituto Brasileiro da Qualidade e Produtividade IBQP Serviço Brasileiro de Apoio às Micro e Pequenas Empresas SEBRAE Serviço Nacional de Aprendizagem Industrial SENAI / PR TNS Dimarso PULS BH d.o.o. Sarajevo Bonilha Comunicação e Marketing S/C Ltda. Chile Universidad del Desarrollo José Ernesto Amorós Daniela Ortega Serviço Social da Indústria SESI / PR InnovaChile de CORFO Opina S.A. Universidad Adolfo Ibáñez Germán Echecopar Carla Bustamante ICARE Regional Teams Arica y Parinacota Regional Universitos Univ. de Tarapacá Regional Members Vesna Karmelic Roberto Gamboa Aguilar Hernando Bustos Andreu Dante Choque Cáceres Área Emprendimiento, Liderazgo y TIC s de la Universidad de Tarapacá Antofagasta Univ. Católica del Norte Gianni Romaní Miguel Atienza Universidad Católica del Norte, DGIP Gobierno Regional, Agencia Regional Desarrollo Productivo Coquimbo Univ. Católica del Norte Karla Soria Universidad Católica del Norte, DGIP Gobierno Regional, Agencia Regional Desarrollo Productivo Valparaíso Univ. Técnica Federico Santa María Cristóbal Fernández Robin Jorge Cea Valencia Juan Tapia Departamento de Industrias y Centro de Ingeniería de Mercados, CIMER, de la Univ. Técnica Federico Santa María El Mercurio de Valparaíso Bío Bío Univ. del Desarrollo Carlos Smith José Ernesto Amorós Daniela Ortega UDD Facultad de Economía y Negocios Araucanía Univ. de la Frontera INCUBATEC Carlos Isaacs Bornand Claudina Uribe Bórquez Franklin Valdebenito Dirección de Innovación y Transferencia Tecnológica de la Universidad de La Frontera 72

74 GEM National Teams 2010 Team Institution National Team Members China Tsinghua University SEM Jian Gao Lan Qin Colombia Universidad de los Andes Rafael Vesga Lina Devis Raúl Fernando Quiroga Financial Sponsors SEM Tsinghua University Universidad de los Andes APS Vendor SINOTRUST International Information & Consulting (Beijing) Co., Ltd. Centro Nacional de Consultoría Universidad ICESI Universidad del Norte Rodrigo Varela V. Luis Miguel Alvarez Alba Tatiana Peña Liyis Gomez Piedad Martinez Carazo Universidad ICESI Universidad del Norte Pontificia Universidad Javeriana Cali Fernando Pereira Alberto Arias Croatia J.J. Strossmayer University in Osijek Slavica Singer Natasa Sarlija Sanja Pfeifer Djula Borozan Suncica Oberman Peterka Denmark University of Southern Denmark Thomas Schøtt Torben Bager Hannes Ottossen Lars Hulgård Kim Klyver Kent Wickstrøm Jensen Roger Spear Linda Andersen Majbritt Rostgaard Evald Suna Løve Nielsen Dominican Republic Pontificia Universidad Católica Madre y Maestra (PUCMM) Guillermo van der Linde Maribel K. Justo Alina Bello Pontificia Universidad Javeriana Cali Fundacion Corona Ministry of Economy, Labour and Entrepreneurship SME Policy Centre CEPOR, Zagreb J.J. Strossmayer University in Osijek Faculty of Economics, Osijek IDEA International Danish Entrepreneurship Academy RUC Roskilde University Centre, Center for Social Entrepreneurship IIIP Grupo Vicini International Financial Centre of the Americas Consejo Nacional de Competitividad Puls, d.o.o., Zagreb Institute for Business Cycle Analysis Gallup República Dominicana Ecuador Escuela Superior Politécnica del Litoral (ESPOL ) ESPAE Graduate School of Management Virginia Lasio Ma. Elizabeth Arteaga Guido Caicedo Escuela Superior Politécnica del Litoral (ESPOL) Survey Data Finland Turku School of Economics Anne Kovalainen Jarna Heinonen Tommi Pukkinen Pekka Stenholm France EMLYON Business School Olivier Torres Danielle Rousson Ministry of Employment and the Economy Ministry of Education Turku School of Economics Caisse des Depots Taloustutkimus Oy CSA Germany Leibniz University of Hannover Federal Employment Agency (BA) Institute for Employment Research (IAB) Rolf Sternberg Udo Brixy Christian Hundt Federal Employment Agency (BA) Institute for Employment Research (IAB) Zentrum fuer Evaluation und Methoden (ZEM), Bonn Greece Foundation for Economic and Industrial Research (IOBE) Stavros Ioannides Aggelos Tsakanikas Stelina Chatzichristou Evaggelia Valavanioti Foundation for Economic and Industrial Research (IOBE) Datapower SA 73

75 GEM National Teams 2010 Team Institution National Team Members Guatemala Francisco Marroquín University Hugo Maul Jershem David Casasola Lisardo Bolaños Georgina Tunarosa Hong Kong The Chinese University of Hong Kong Hugh Thomas Kevin Au Louis Leung Mingles Tsoi Bernard Suen Wilton Chau Florence Ho Rosanna Lo Le Zheng Wang Weili Hungary University of Pécs, Faculty of László Szerb Business and Economics Attila Varga József Ulbert Zoltan J. Acs Siri Terjesen Gábor Márkus Péter Szirmai Attila Petheő Katalin Szakács Iceland Reykjavik University Rögnvaldur Sæmundsson Silja Björk Baldursdóttir Financial Sponsors Francisco Marroquín University Shenzhen Academy of Social Sciences Hong Kong Business Intermediary Co. Ltd. National Office for Research and Technology George Mason University University of Pécs, Faculty of Business and Economics Reykjavik University APS Vendor Pablo Pastor Consumer Search Szocio Gráf Piacés Közvélemény kutató Intézet Capacent Gallup Iran University of Tehran Abbas Bazargan Caro Lucas Nezameddin Faghih A.A. Moosavi Movahedi Leyla Sarfaraz A. Kordrnaeij Jahangir Yadollahi Farsi M. Ahamadpour Daryani S. Mostafa Razavi Mohammad Reza Zali Mohammad Reza Sepehri Israel The Ira Center of Business, Technology and Society, Ben Gurion University of the Negev Ehud Menipaz Yoash Avrahami Miri Lerner Yossi Hadad Dov Barak Italy EntER Bocconi University Guido Corbetta Giovanni Valentini Iran s Ministry of Labour and Social Affairs Iran s Labour and Social Security Institute (LSSI) The Ira Center for Business, Technology & Society, Ben Gurion University of the Negev Sami Shamoon College of Engineering Rotem Park NGO for Entrepreneurship Encouragement Atradius Ernst & Young Dr. Mohammad Reza Zali The Brandman Institute Target Research 74 Jamaica University of Technology, Jamaica Girjanauth Boodraj Mauvalyn Bowen Joan Lawla Marcia McPherson Edwards Vanetta Skeete Horace Williams Japan Keio University Musashi University Shobi University Takehiko Isobe Noriyuki Takahashi Tsuneo Yahagi Republic of Korea Jinju National University Sung sik Bahn Sang pyo Kim Kyoung mo Song Dong whan Cho Jong hae Park Min Seok Cha Faculty of Business and Management, University of Technology, Jamaica Venture Enterprise Center Ministry of Economy, Trade and Industry Small and Medium Business Administration (SMBA) Cashmere International Limited Social Survey Research Information Co., Ltd (SSRI) Hankook Research Co.

76 GEM National Teams 2010 Team Institution National Team Members Latvia The TeliaSonera Institute at the Olga Rastrigina Stockholm School of Economics Alf Vanags in Riga Anders Paalzow Vyacheslav Dombrovsky Arnis Sauka Malaysia University Tun Abdul Razak Siri Roland Xavier Dewi Amat Sapuan Ainon Jauhariah Abu Samah Leilanie Mohd Nor Mohar Yusof Middle East and North Africa International Development Research Centre (IDRC) Lois Stevenson Susan Joekes Edgard Rodriquez Abderrahmane Abedou Ahmed Bouyacoub Hala Hattab Matthias Hühn Tony Feghali Yusuf Sidani Ghassan Omet Adel Bino Mohamed Derrabi Lamia Dandoune Taha Ahmed Al Fusail Abdul Karim Sayaghi Yousef Daoud Tareq Sadeq Alaa Tartir Ruba Adil. Shanti Netherlands EIM Business and Policy Research Jolanda Hessels Sander Wennekers Chantal Hartog Niels Bosma Roy Thurik André van Stel Ingrid Verheul Philipp Koellinger Peter van der Zwan Norway Bodo Graduate School of Business Lars Kolvereid Erlend Bullvaag Bjorn Willy Aamo Eirik Pedersen Terje Mathisen Panama Acelerador de Empresas de Ciudad del Saber Federico Fernandez Manuel Lorenzo Financial Sponsors TeliaSonera AB University Tun Abdul Razak International Development Research Centre (IDRC) Dutch Ministry of Economic Affairs Ministry of Trade and Industry Innovation Norway Kunnskapsparken Bodo AS, Center for Innovation and Entrepreneurship Kunnskapsfondet Nordland AS Bodo Graduate School of Business Cámara de Comercio e Industrias de Panamá Cable & Wireless Panamá APS Vendor SKDS Rehanstat Nielsen Stratus TNS Gallup IPSOS IESA Panamá Fundación de Estudios Avanzados de Gerencia Manuel Arrocha Michael Penfold Peru Universidad ESAN Jaime Serida Oswaldo Morales Keiko Nakamatsu Liliana Uehara APC Asociación Panameña de Crédito Almacenajes, S.A. Banco Interamericano de Desarrollo Universidad ESAN Imasen 75

77 GEM National Teams 2010 Team Romania Russia Institution Faculty of Economics and Business Administration, Babeş Bolyai University Saint Petersburg Team Graduate School of Management, Saint Petersburg National Team Members Györfy Lehel Zoltán Matiş Dumitru Nagy Ágnes Pete Ştefan Szerb László Mircea Comşa Ilieş Liviu Benyovszki Annamária Petru Tünde Petra Juhász Jácint Matiş Eugenia Nagy Zsuzsánna Ágnes Alina Solovă stru Mircea Solovă stru Olga Verhovskaya Maria Dorokhina Financial Sponsors Asociatia Pro Oeconomica Babeş Bolyai University, Faculty Of Economics And Business Administration OTP Bank Romania SA East Consulting SRL Lacu Roşu SC Nova Construct SRL Savilcom SRL Graduate School of Management at Saint Petersburg State University APS Vendor Metro Media Transilvania Saudi Arabia Serbia Slovenia South Africa Moscow Team State University Higher School of Economics, Moscow The National Entrepreneurship Center Alfaisal University University of Novi Sad The Faculty of Economics Subotica Institute for Entrepreneurship and Small Business Management, Faculty of Economics and Business, University of Maribor The UCT Centre for Innovation and Entrepreneurship, Graduate School of Business, University of Cape Town Alexander Chepurenko Olga Obraztsova Maria Gabelko Tatiana Alimova Julia Filatova Kate Murzacheva Munira A. Al Ghamdi Maher Alodan Faisal A. Al Kadi Norman Wright Sulaiman Al Sakran Mohammad S Khorsheed Dusan Bobera Bozidar Lekovic Nenad Vunjak Pere Tumbas Sasa Bosnjak Slobodan Maric Miroslav Rebernik Polona Tominc Ksenja Pušnik Katja Crnogaj Mike Herrington Jacqui Kew Penny Kew Spain Instituto de Empresa Ignacio de la Vega Alicia Coduras Isabel Gonzalez Cristina Cruz Rachida Justo State University Higher School of Economics Ministry of Economic Development of Russian Federation The Centennial Fund/National Entrepreneurship Center Executive Council of Vojvodina Province Department for Economy Ministry of the Economy Slovenian Research Agency Finance Slovenian Business Daily Swiss South African Cooperation Initiative Standard Bank of South Africa Small Enterprise Development Agency DGPYMES Fundación Cultural Banesto Fundación Incyde IE Business School Levada Center IPSOS Marketing Agency Drdrazen d.o.o. Subotica RM PLUS Nielsen South Africa Instituto Opinòmetre S.L. 76 Regional Teams Andalucía Regional Universities Cádiz Regional Team Directors José Ruiz Navarro Junta de Andalucía Unicaja Asturias Universidad De Oviedo Juan Ventura Victoria Gob. del Principado de Asturias Aragón Universidad de Zaragoza Lucio Fuentelsaz Gob. de Aragón Dpto, Industria, Comercio y Turismo Fundación Emprender en Aragón Instituto Aragones Fomento Consejo Aragones Cámaras de Comercio

78 GEM National Teams 2010 Team Canary Islands Institution Las Palmas & La Laguna Universidad National Team Members Rosa M. Batista Canino Financial Sponsors Caja Canarias Gobierno de Canarias, Promoción Económica y Servicio de Empleo Fondo Social Europeo Cámara Comercio, Industria y Navegación de las Palmas Cabildo de Gran Canaria APS Vendor Cantabria Universidad De Cantabria Cátedra Pyme de la Universidad de Cantabria Fco. Javier Martínez Santander Gob. Regional Cantabria Consejería de Economía y Hacienda Grupo Sordecan Fundación UCEIF Castilla y Leon Universidad de León Mariano Nieto Antolín Junta Castilla y León ADE Inversiones y Servicios Centro Europeos de Empresas e Innovación de Castilla y León Universidad de León Castilla la Mancha Universidad Castilla la Mancha Miguel Ángel Galindo Fundación Rayet Parque Científico de Albacete Caja Castilla La Mancha IMPEFE Ayuntamiento de Albacete Universidad Castilla la Mancha Diputación Provincial Allbacete SEPECAM UGT (Iniciativas Futuro) Catalonia Autónoma de Barcelona Carlos Guallarte Diputación de Barcelona Departamento de Trabajo Generalitat de Catalunya C. Valenciana Universidad Miguel Hernández José Mª Gómez Gras Air Nostrum IMPIVA Extremadura Galicia Fundación Xavier de Salas Universidad de Extremadura CEEI Galicia CEG Grupo de investigación Métodos y Gestión Empresas de la Universidad Santiago Compostela Dirección Xeral do Emprego de la Xunta de Galicia Ricardo Hernández Araceli de Lucas Junta Extremadura Universidad Extremadura Central Nuclear Almaraz Sodiex, Sofiex Arram Consultores, CCOO U.R Extremadura, Urvicasa Caja Rural de Extremadura, Palicrisa Fundación Academica Europea de Yuste Grupo Alfonso Gallardo Infostock Europa Extremadura Cámara Comercio Badajoz y Cámara Comercio Cáceres UGT Extremadura, El Periódico Extremadura, Hoy Diario de Extremadura, García Plata y Asociados, Quesería Pérez Andrada, Fomento Emprendedores Confederación Empresarios Galicia (CEG) CEEI Galicia SA (BIC Galicia) Grupo de investigación Métodos y Gestión Empresas de la Universidad Santiago Compostela Madrid Autónoma de Madrid Eduardo Bueno IMADE Fundación General Universidad Autónoma de Madrid 77

79 GEM National Teams 2010 Team Institution National Team Members Financial Sponsors Murcia Universidad de Murcia Antonio Aragón Fundación Caja Murcia Consejería de Economía, Empresa e Innovación Instituto Fomento región de Murcia. Centro Europeo de Empresas e Innovación de Murcia Universidad Murcia Navarra Universidad Pública de Navarra Miren Sanz Gob. Navarra, Servicio Navarro de Empleo Centro Europeo de Empresas e Innovación de Navarra Servicio Navarro de Empleo Basque Country Orkestra Iñaki Peña Eusko Ikaskuntza Universidad de Deusto SPRI, Gobierno Vasco Universidad Basque Country Diputación Foral Álava Universidad Mondragón Diputación Foral Bizkaia Diputación Foral Gipuzkoa Fundación Emilio Soldevilla Ceuta Melilla Fundación Escuela de Negocios de Andalucía Consejería de Economía, Empleo y Turismo Fundación Escuela de Negocios de Andalucía Lázaro Rodríguez Lázaro Rodriguez PROCESA Fundación Escuela Negocios Andalucía Consejería de Economía, Empleo y Turismo Fundación Escuela de Negocios Andalucía APS Vendor Switzerland Syria School of Business Administration (SBA Fribourg) Syria Trust for Development Syrian Young Entrepreneurs Association (SYEA) University of Kalamoun Rico J. Baldegger Andreas A. Brülhart Mathias J. Rossi Etienne Rumo Patrick E. Schüffel Thomas Straub Muriel Berger Majd Haddad Nader Kabbani Yamama Al Oraibi Leen Al Habash Abdulsalam Haykal Hani Tarabichi KTI /CTI IDRC Syrian Young Entrepreneurs Association (SYEA) Syria Trust for Development DemoSCOPE Nielsen / Acumen Kingdom of Tonga UNITEC Robert Davis Malama Solomona Asoka Gunaratne Stephen Cox Judith King AusAID Creatrix International / Kaha utonga Tunisia Institut des Hautes Etudes Commerciales Sousse Howard Frederick Stephen Haslett Tania Wolfgramm Faysal Mansouri Lotfi Belkacem GTZ Programme d Appui à l Entrepreneuriat et à l Innovation Optima Uganda Makerere University Business School (MUBS) Arthur Sserwanga Rebecca Namatovu Dawa Sarah Kyejjusa Laura Orobia Peter Rosa Waswa Balunywa Makerere University Business School Bank of Uganda Makerere University Business School 78

80 GEM National Teams 2010 Team Institution National Team Members United Arab Emirates Zayed University Declan McCrohan Murat Erogul Nico Vellinga Qingia Tong Maijha Qurwash United Kingdom Aston University Jonathan Levie Mark Hart Erkko Autio Liz Blackford Michael Anyadike Danes Alpheus Tlhomole Aloña Martiarena Mohammed Shamsul Kharim Yasser Bhatti United States Babson College Julio de Castro Abdul Ali I. Elaine Allen Bill Bygrave Candida Brush Marcia Cole Lisa DiCarlo Julian Lange Monica Dean Joseph Onochie Ivory Phinisee Edward Rogoff Al Suhu Uruguay University of Montevideo Leonardo Veiga Fernando Borraz Pablo Regent Adrián Edelman Alvaro Cristiani Cecilia Gomeza Venezuela IESA Centro de Emprendedores Federico Fernandez Rebeca Vidal Aramis Rodriguez Nunzia Auletta GEM Global Coordination Team Kristie Seawright Niels Bosma Mick Hancock Alicia Coduras Marcia Cole Yana Litovsky Chris Aylett Jackline Odoch Jeff Seaman Financial Sponsors Mohammed Bin Rashid Establishment for SME Development Khalifa Fund to Support and Develop Small and Medium Enterprises Department of Business, Innovation and Skills English RDAs Invest NI Welsh Assembly Government Enterprise UK PRIME Birmingham City Council Belfast City Council Enterprise Northern Ireland Hunter Centre for Entrepreneurship, University of Strathclyde Babson College Baruch College University of Montevideo Mercantil Servicios Financieros Fundacion Iesa Petrobras Energía Venezuela APS Vendor IPSOS IFF Research Ltd. OpinionSearch Inc. Equipos Mori Datanalisis 79

81 Appendix Table A1 - Comparison of Entrepreneurial Activity for 2007, 2008 and 2009 by Phase of Economic Development for the Percentage of the Age Group Nascent Entrepreneurship Rate % New Business Ownership Rate % Early-Stage Entrepreneurial Activity (TEA) % Established Business Ownership Rate % Discontinued Businesses % Necessity-Driven TEA % Improvement-Driven TEA % Efficiency-Driven Economies Argentina Brazil Chile Croatia Dominican Republic Hungary Latvia Peru Romania Russia Serbia Uruguay average (unweighted) Innovation-Driven Economies Belgium Denmark Finland France Greece Iceland Israel Italy Japan Netherlands Norway Slovenia Spain UK United States * average (unweighted) *Established business entrepreneurial prevalence rates were underestimated for the United States in Source: GEM 2007, 2008 and 2009 Adult Population Surveys (APS) 80

82 GEM Sponsors GERA and GEM The Global Entrepreneurship Research Association (GERA) is, for formal constitutional and regulatory purposes, the umbrella organization that hosts the GEM project. GERA is an association formed of Babson College, London Business School and representatives of the Association of GEM national teams. The GEM program is a major initiative aimed at describing and analyzing entrepreneurial processes within a wide range of countries. The program has three main objectives: To measure differences in the level of entrepreneurial activity between countries To uncover factors leading to appropriate levels of entrepreneurship To suggest policies that may enhance the national level of entrepreneurial activity New developments and all global, national and special topic reports can be found at The program is sponsored by Babson College and London Business School. BABSON COLLEGE Babson College in Wellesley, Massachusetts, USA, is recognized internationally as a leader in entrepreneurial management education. Babson grants BS degrees through its innovative undergraduate program and grants MBA and custom MS and MBA degrees through the F.W. Olin Graduate School of Business at Babson College. Babson Executive Education offers executive development programs to experienced managers worldwide. For information, visit baruch college Baruch College has a 160-year history of excellence in public higher education with an emphasis on business. A senior college in the City University of New York system, Baruch College offers undergraduate and graduate programs of study through its three schools: the Zicklin School of Business, the Weissman School of Arts and Sciences and the School of Public Affairs. Housed at the Zicklin School is the Lawrence N. Field Center for Entrepreneurship, a model of entrepreneurship education built around the collaboration of an institution of higher education, government and the private sector. For information, visit 81

83 Contacts For more information on the Global Entrepreneurship Monitor 2008 National Entrepreneurial Assessment for the United States of America Executive Report, contact: Julio De Castro For more information on the Global Entrepreneurship Monitor or for more copies of this report, contact: Marcia Cole Telephone: GEM Global Reports, National Team Reports, Public Data Sets (selected), events information, etc., are available on the GEM website: To download copies of this report and to access select data sets, please visit the GEM website: Nations not currently represented in the GEM Consortium may express interest in joining and ask for additional information by ing Executive Director Kristie Seawright at or Marcia Cole at 82

84 About the Authors Abdul Ali Abdul Ali is the President s Term Chair and an Associate Professor of Marketing at Babson College. Earlier, he served as Chair of the Marketing Division for six years ( ) at Babson College. His teaching and research interests include entrepreneurial marketing, new product management, marketing research methods, marketing strategy and marketing high-tech products. I. Elaine Allen I. Elaine Allen is the Research Director of the Arthur M. Blank Center for Entrepreneurship and an Associate Professor of Statistics and Entrepreneurship at Babson College. A Fellow of the American Statistical Association, she is also a founder of StatSystems, ARIAD Pharmaceuticals and MetaWorks, Inc. William D. Bygrave William D. Bygrave is professor emeritus at Babson College. In 1997, he co-founded GEM to study the entrepreneurial competitiveness of nations. He is delighted that GEM is now a thriving consortium of more than 60 nations that comprise about 95 percent of the world s GDP and more than two-thirds of its population. Julio De Castro Julio De Castro is Professor of Entrepreneurship and Lewis Family Distinguished Professor at Babson College. He has served on the Board of Governors of the Academy of Management and is president of the Iberoamerican Academy of Management. He is associate editor of The Journal of Small Business Management and serves on the editorial boards of The Journal of Management Studies, Revista de Empresa, Management Research, The Journal of High Technology Management Research, Journal of World Business, and Entrepreneurship Theory and Practice. Previously, he served on the editorial board of the Academy of Management Journal. Julian Lange Julian Lange is the Governor Craig R. Benson Professor of Entrepreneurship and Public Policy at Babson College, where he leads the public policy entrepreneurship curriculum initiative and teaches MBA, undergraduate and executive education courses. An experienced entrepreneur and adviser to private sector firms and public agencies, he served as CEO of Software Arts, creator of VisiCalc the first electronic spreadsheet. Thomas Lyons Thomas Lyons is the Lawrence N. Field Family Chair in Entrepreneurship and Professor of Management at Baruch College, City University of New York, where he teaches courses in social entrepreneurship and entrepreneurship and community development. He is the author of nine books and numerous articles on economic development and entrepreneurship. 83

85 About the Authors Ivory Phinisee Ivory Phinisee was previously a manager of International Demand Analysis & Forecasting at AT&T. Currently, he is a Research Associate at the Lawrence N. Field Center for Entrepreneurship at Baruch College, CUNY. Edward Rogoff Edward Rogoff is Professor of Management and Chair of the Management Department at Baruch College, CUNY. He is the author of Bankable Business Plans and The Entrepreneurial Conversation along with many articles related to entrepreneurship. Joseph Onochie Joseph Onochie is the Academic Director of the Executive MBA program and an Associate Professor of Finance at Baruch College, CUNY. He has also served as a consultant and adviser to financial services firms, investments banks and hedge funds. John R. Whitman John R. Whitman is Adjunct Lecturer at Babson College, where he teaches Social Entrepreneurship and Options for Creating Social and Economic Value. His research interests include entrepreneurship in cooperatives, nonprofit organizations, and philanthropic foundations, especially in international comparative contexts. 84

86 Endnotes i These phases coincide with the classification by the most recent Global Competitiveness Report into factor driven, efficiency driven and innovation driven economies. See Schwab (2009). ii Evidence is documented by, e.g., Carree and Thurik (2003), Acs (2006) and Audretsch (2007). iii See Wennekers, Van Stel, Thurik and Reynolds (2005) and Gries & Naude (2008). iv In the construction of the annual Global Competitiveness Index, weights are used in accordance with these notions. Thus, for factor-driven economies, the state of basic requirements adds most to the overall index. v See e.g., Gartner (1986) and Shane and Venkataraman (2000). vi See vii Most new businesses do not survive beyond three or four years. This is the main rationale for the choice of 42 months as the cut-off period. However, the choice of 42 months reflects also operational issues. According to Reynolds et al.: The relevant interview question asked only the year when salary and wage payments were initiated and most surveys occurred in the summer months; so the alternatives for choosing a new firm age were 1.5 years, 2.5 years, 3.5 years, etc. The shortest time frame that would provide enough cases for stable prevalence rates with a total sample of 2,000 seemed to occur at 3.5 years. Conceptually, any time period under five years seemed satisfactory so this age was considered an appropriate trade-off between conceptual and operational considerations in the early years of the project. There has been no compelling reason to adjust this criteria and a desire for a stable time series has led to its continued use. It should be considered a procedure to capture existing firms less than three or four years old. [Reynolds, P.D., Bosma, N.S., Autio, E. et al. (2005).] viii The sample sizes in the GEM 2009 study typically range from 2,000 to 3,500. Notable exceptions are Spain (29,000 respondents) and the UK (30,000 respondents). ix Global Entrepreneurship Monitor 2009 Global Executive Report, page 28. x Making Banks Lend, Bloomberg Businessweek, June 28 to July 4, xi Courtney Rubin, Bernanke Urges Banks to Up Small Business Lending, Inc., July 13, xii Bleak Outlook for Small Businesses and Job Creation; Where Obama Went Wrong, and What to do About It, xiii Big Banks Small-Business Lending Priorities, John Tozzi, xiv xv Data are from the Federal Financial Institutions Examination Council (FFIEC) Consolidated Reports of Condition and Income (Call Report), where loans to small businesses, as stated in the reporting forms FFIEC 031 and 041, schedule RC-C, part II, are defined as loans with original amounts of $1 million or less that are secured by nonfarm nonresidential properties or are commercial and industrial loans, plus loans with original balances of $500,000 or less that are secured by farmland or are for agricultural production. xvi Most of the venture capital data in this section are from the National Venture Capital Association Yearbook 2010, New York: Thomson Venture Economics. xvii National Venture Capital Association Yearbook 2010, p.9, New York: Thomson Venture Economics. xviii National Venture Capital Association, xix National Venture Capital Association: Yearbook 2009, New York: Thomson Venture Economics. xx Ibid. xxi CNNMoney.com February 12, 2008, Al Gore s next act: Planet-saving VC, xxii National Venture Capital Association Yearbook 2010, p.50, New York: Thomson Venture Economics. 85

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