Global Entrepreneurship Monitor: Data Collection Design and Implementation 1998–2003

The Global Entrepreneurship Monitor research program was designed as a comprehensive assessment of the role of entrepreneurship in national economic growth. The conceptual model reflected in a wide range of factors associated with national variations in entrepreneurial activity and the major contextual features. Empirical tests of the many relationships in the model required four major data collection activities: adult population surveys, unstructured interviews with national experts, self-administered questionnaires completed by national experts, and assembly of relevant standardized measures from existing cross-national data sets. Adult population surveys were implemented to identify those entrepreneurially active, which required a set of precise criteria and careful processing to ensure harmonized counts and prevalence rates across 41 countries. Existing evidence on measures of reliability indicates that the measures met contemporary standards and the project was cost-effective.     

Designing a global standardized methodology for measuring social entrepreneurship activity: the Global Entrepreneurship Monitor social entrepreneurship study

Although there is a high level of practitioner, policymaker, and scholar interest in social entrepreneurship, most research is based on case studies and success stories of successful social entrepreneurs in a single country. We develop a methodology to measure population-based social entrepreneurship activity (SEA) prevalence rates and test it in 49 countries. Our results provide insights into institutional and individual drivers of SEA. Using the Global Entrepreneurship Monitor (GEM) methodology of Total Entrepreneurial Activity (TEA), we find that countries with higher rates of traditional entrepreneurial activity also tend to have higher rates of social entrepreneurial activity. We develop a broad definition of social entrepreneurship and then explore types based on social mission, revenue model, and innovativeness.     

Success and Risk Factors in the Pre-Startup Phase

Why does one person actually succeed in starting a business, while a second person gives up? In order to answer this question, a sample of 517 nascent entrepreneurs (people in the process of setting up a business) was followed over a three-year period. After this period, it was established that 195 efforts were successful and that 115 startup efforts were abandoned. Our research focuses on estimating the relative importance of a variety of approaches and variables in explaining pre-startup success. These influences are organized in terms of Gartner’s (Academy of Management Review 10(4), 696–706 [1985]) framework of new venture creation. This framework suggests that start-up efforts differ in terms of the characteristics of the individual(s) who start the venture, the organization that they create, the environment surrounding the new venture, and the process by which the new venture is started. Logistic regression analyses are run for the sample as a whole as well as for subgroups within the sample, namely for those with high ambition vs. low ambition and for those with substantial vs. limited experience. The results point to the importance of perceived risk of the market as a predictor of getting started vs. abandoning the startup effort.     

The Value of Human and Social Capital Investments for the Business Performance of Startups

Small Business Economics - We investigate the manifold posed question: “To what extent does investment in human and social capital, besides the effect of ‘talent’, enhance...     

Modelling Entrepreneurship: Unifying The Equilibrium and Entry/Exit Approach

Two approaches can be distinguished with respect to modelling entrepreneurship: (i) the approach focusing on the net development of the number of entrepreneurs in an equilibrium framework and (ii) the approach focusing on the entries and exits of entrepreneurs. In this paper we unify these approaches to arrive at a model explaining the equilibrium and actual number of entrepreneurs and the entry and exit rate of entrepreneurs simultaneously and consistently. We apply our unified approach to the Netherlands using self-employment data for the 1960–1999 period. We find error-correction of about 20% per year and a very different situation in terms of disequilibrium before and after the early 1980s. Periods of high unemployment appear to be characterized by both high entry and high exit rates.     

应运而生的产品生命周期管理

由于市场、技术、消费观念以及监管法规等多种因素的影响,制造行业和企业正在经历着一场深刻的变革。这场变革对企业产品及其管理带来了全方位的影响,包括产品设计、制造、市场投放和服务等。换言之,日益缩短的产品生命周期、全球化竞争、供应链的复杂性以及不断增长的客户需求,使新产品的研发和市场投放更困难,更具挑战性。     

Creative destruction and regional productivity growth: evidence from the Dutch manufacturing and services industries

Do firm entry and exit improve the competitiveness of regions? If so, is this a universal mechanism or is it contingent on the type of industry or region in which creative destruction takes place? This paper analyses the effect of firm entry and exit on the competitiveness of regions, as measured by total factor productivity (TFP) growth. Based on a study across 40 regions in the Netherlands over the period 1988–2002, we find that firm entry is related to productivity growth in services, but not in manufacturing. The positive impact found in services does not necessarily imply that new firms are more efficient than incumbent firms; high degrees of creative destruction may also improve the efficiency of incumbent firms. We also find that the impact of firm dynamics on regional productivity in services is higher in regions exhibiting diverse but related economic activities.     

Institutions,entrepreneurship, and economic growth in Europe

Institutions have a decisive impact on the prevalence and nature of entrepreneurship. To date, the impact of institutions on (productive) entrepreneurship and the effects of entrepreneurship on economic growth have largely been investigated in isolation. In this paper, we bring together institutions, entrepreneurship, and economic growth using a parsimonious growth model in a 3SLS specification. In our first stage, we regress multiple measures of entrepreneurial activity on institutional proxies that are known to correlate with more productive forms of entrepreneurial activity. Using the fitted values of this first-stage regression as our proxy for productive entrepreneurship, we can then estimate a panel growth regression following Islam (1995) in a second stage. The third stage then optimizes the estimation of the two equations simultaneously. Our results show that productive entrepreneurship contributes to economic growth. In our set of proxies for institutional quality, financial stability, small government, and perceived start-up skills are the most important predictors of such productive entrepreneurship.     

Success and Risk Factors in the Pre-Startup Phase

Why does one person actually succeed in starting a business, while a second person gives up? In order to answer this question, a sample of 517 nascent entrepreneurs (people in the process of setting up a business) was followed over a 3-year period. After this period, it was established that 195 efforts were successful and that 115 start up efforts were abandoned. Our research focuses on estimating the relative importance of a variety of approaches and variables in explaining pre-start-up success. These influences are organized in terms of Gartner’s (1985) framework of new venture creation. This framework suggests that start-up efforts differ in terms of the characteristics of the individual(s) who start the venture, the organization that they create, the environment surrounding the new venture, and the process by which the new venture is started. Logistic regression analyses are run for the sample as a whole as well as for subgroups within the sample, namely for those with high ambition versus low ambition and for those with substantial versus limited experience. The results point to the importance of perceived risk of the market as a predictor of getting started versus abandoning the start up effort.