How entrepreneurial characteristics influence company creation: a cross-national study of 22 countriestested with panel data methodology/Verslumo charakteristiku itaka imones kurimui: studijos, atliktos 22 valstybese, rezultatu pristatymas.
Research into entrepreneurs is critical for understanding the creation of enterprises (e.g., Frederking 2004; Gruner 2006; Shirokova, Knatko 2008; Uhlaner, Thurik 2007; Westhead et al. 2005), especially in contexts marked by a large number of small and medium-sized enterprises (SMEs). In these scenarios, the type of employment often determines the evolution of the productive unit (Gruner 2006; Johnson et al. 2006; Nikolaus, Christian 2004; Van Praag, Versloot 2007), and therefore, extensive research focuses on assessing the figure of the entrepreneur (e.g., Arenius, Minniti 2005; De Jorge Moreno, Laborda Castillo, De Zuani Masere 2010; Shirokova, Knatko 2008; Wagner 2007), including how the entrepreneur's profile affects the recognition and exploitations of business opportunities (e.g., Zhao et al. 2009).
This line of research encompassess different theoretical approaches (e.g., economic, sociological, psychological, managerial) but still is not exempt from certain disadvantages, especially pertaining to the methodology and type of sample used to study the associated phenomena (Reynolds 1997; Van Praag, Versloot 2007; Volkman 2004). Most prior work considers relatively small samples from a single country, without noting their gender or socioeconomic environment. Yet the entrepreneur's attitudes, approaches, values, and satisfaction with the status quo have remarkable impacts on the creation of the company (e.g., Arenius, Minniti 2005; Frank et al. 2007; Frederking 2004; Furkukawa et al. 2007; McCline et al. 2000; Uhlaner, Thurik 2007).
Moreover, prior research has not clearly defined the various stages of entrepreneur-ship, combining nascent, new, and consolidated entrepreneurs within the same category, which may generate biases in the results. The lack of distinction between the previous stages of the entrepeneurial activity likely creates bias, and previous research (e.g., Reynolds et al. 2005) notes the need to consider different types of entrepreneurs according to the time passed since the creation of the business. First, nascent entrepreneurs have completed the first process of entrepreneurship, moving from the conception of the business into the gestation or start-up process. They generally have paid salaries and wages for less than three months. Second, new entrepreneurs have been in business (i.e., paid salaries and wages) for at least three months but less than 3.5 years. Third, established entrepreneurs have been in business for a while, that is, at least 3.5 years.
This study therefore identifies the sociodemographic profiles of entrepreneur to determine how they affect enterpreneurial behavior. Furthermore, it employs and extends the scheme proposed by Arenius and Minniti (2005) and incorporates contextual variables related to the environment. In particular, this study includes nascent and new entrepreneurs, that is, those whose companies' duration does not exceed 42 months. The sample of entrepreneurs spans 22 diverse countries, located on four continents, and therefore can identify cross-national differences according to the gender of the entrepreneurs and their income levels (measured as each country's gross domestic product [GDP]). Finally, to analyze these data, this study uses a panel methodology, which provides added interest by combining temporal series and cross-sectional data.
This article consists of five further sections. The next section reviews theory related to entrepreneurship and related hypotheses for this investigation. After a description of the methodology, the following section details and discusses the main results. Finally, the conclusion synthesizes the principal implications and proposes a series of recommendations for both entrepreneurs and the agents in charge of developing economic policies.
2. Conceptual framework and hypotheses development
2.1. The influence of the entrepreneur's age
Entrepreneurship literature highlights the important impact of the age of the entrepreneur with regard to his or her experience and vitality (De Jorge Moreno et al. 2010; Sandberg, Hofer 1987). In general, older people likely enjoy an advantage derived from accumulated experience, such that the likelihood of creating a new company increases among older entrepreneurs (Hesselset et al. 2008; Levesque, Minniti 2006). Yet other research shows that younger people tend to possess more energy, vigor, and enthusiasm, which in combination with knowledge and open mental attitudes enables them to seek, recognize, and develop more innovative business opportunities with economic growth potential (Grilo, Irigoyen 2006; Hessels et al. 2008). Therefore, this study posits that over the course of time, a person accumulates experience but also loses aptitude for recognizing opportunities (e.g., possesses less updated knowledge, prefers to avoid risk, suffers personal and psychological limitations). In turn, this study offers a nondirectional hypothesis regarding the influence of the age of an entrepreneur:
[H.sub.1]: The age of the entrepreneur influences in the creation of a business.
2.2. The influence of the entrepreneur's education and experience
Education also appears associated with entrepreneurship, especially with regard to the benefits of education for the level of managerial action and the profitability of the company, among other indicators (Arenius, De Clercq 2005; De Jorge Moreno et al. 2010; Naude et al. 2008; Sapienza, Grimm 1997). Education or training generally is associated with the development of capacities (e.g., analytical skills, information processing aptitude, idea association) that contribute to the recognition and development of innovative business opportunities. People with an advanced degree are more inclined to introduce innovations into the market (e.g., Klandt 2004), develop intentions to create their own companies (e.g., Oosterbeek et al. 2009; Peterman, Kennedy 2003; Raguseo 2009; Zuperka 2010), and detect business opportunities with innovative potential (e.g., Arenius, De Clercq 2005; Souitaris et al. 2007).
Because, as some authors indicate, entrepreneurial success depends on both the entrepreneur's personal characteristics and professional aptitude acquired through education (Naude et al. 2008), most entrepreneurs possess a high level of education. Roberts (1970) finds that the founders of high-tech companies tend to possess at least a master's degree, and Cooper and Dunkelberg (1987) note that entrepreneurs' education level is higher than that of the population in general. Gelderen et al. (2001) and Naude et al. (2008) also suggest that most entrepreneurs possess a high education level.
Noting these concepts, and considering the expansion of education in recent years, which has demonstrated the positive effects of higher education on the ability to start a company, especially in competitive environments, we propose:
[H.sub.2]: The younger the entrepreneur, the greater his or her consciousness of the benefits for higher education for creating a new business.
Likewise, previous experience has great relevance for the emergence of new companies (De Jorge Moreno et al. 2010; Hyytinen, Ilmakunnas 2007; Kolvereid, Isaksen 2006; Stam et al. 2008). Businesspeople, like anyone, learn from their successes and failures, which then mold their knowledge, cognitive processes, and successive options (Baron 2004; Mitchell et al. 2004; Ucbasaran, Westhead 2002).
Some findings reveal that a founder's education, experience, and previous knowledge relate positively to both the emergence of companies and managerial consolidation (Davidsson 2006; Hyytinen, Ilmakunnas 2007; Lee, Tsang 2001; Naude et al. 2008; Sapienza, Grimm 1997; Stam et al. 2008; Ucbasaran, Westhead 2002). Specifically, businesspeople with experience (both managerial and labor) should recognize more innovative business opportunities (Ucbasaran, Westhead 2002) and be more inclined to undertake related work activities (Cooper, Dunkelberg 1987; Kolvereid, Isaken 2006; Stam et al. 2008), often in response to an interaction with the environment that generates an impulse to explore an idea (Alas 2004; Zander 2004). For example, preexisting and ongoing relations with important clients might provide valuable opportunities for the entrepreneur to join a commercial network and attract loyal clients in the long term (Ripolles, Blesa 2006; Shrader et al. 2000). In this case, experience provides a valuable human capital resource for company creation and consolidation (Peter, Vertinsky 2008). Therefore,
[H.sub.3]: The degree of previous experience of the entrepreneur (entrepreneurial, technical, or managerial) positively influences business startup.
To confirm the proposed hypotheses empirically, this study employs the Global Entrepreneurship Monitor (GEM) database and gathers a sample of 22 countries. This database and the associated project enjoy a strong reputation, especially for entrepreneurship studies, and provides nearly innumerable possibilities for obtaining information partitioned and differentiated according to diverse variables.
This study focuses on the period between 2002 and 2006, during which time all 22 sampled countries took part constantly in the GEM project. According to the GEM classification by income (i.e., levels of GDP per capita), this sample includes high, medium, and low income countries, as follows:
* High income: United States, Canada, United Kingdom, Denmark, Finland, France, Belgium, Germany, Ireland, Italy, Japan, The Netherlands, Norway, Singapore, Slovenia, Spain, Sweden.
* Medium and low income: Argentina, Brazil, China, Croatia, South Africa.
The variables in the empirical model reflect information obtained in 589,377 surveys of persons between the ages of 18 and 64 years who declared their involvement in an entrepreneurial project. Table 1 outlines these variables.
The proposed general model includes panel data and fixed effects:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (1)
This model also can be disaggregated into separate models for men and women to discern any differences related to this factor. Table 2 contains the variables used in these models.
The equations for these gender-specific models are as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]; (2)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (3)
Panel data methodology, which combines temporal series and cross-sectional data, is appropriate for estimating the behavior of nascent and new entrepreneurs across countries, because it can offer estimations even when no observable heterogeneities emerge for each country or across time. In addition, it can identify individual-specific effects for each entrepreneur pertaining to how he or she decides to create a company.
4. Analysis of results and discussion
The estimations of Equations 1-3 rely on estimated generalized least squares (EGLS), corrected for heteroscedasticity by White's method using the variables in the general model (see Table 3). The variance explained by the endogenous variables TEAit, TEAMALit, and TEAFEMit are 90.75%, 83.9%, and 88.3% respectively.
Thus, age, educational level, and experience all significantly and positively influence the likelihood of creating a company, in support of the hypotheses. The next step analyzes the average elasticities of the variables of every model, as listed in Table 4.
These elasticities reveal the order of importance of the characteristics of nascent and new entrepreneurs across 22 countries: previous experience of the entrepreneur, with an elasticity of 0.4818%; age of the entrepreneur, especially for entrepreneurs between 18 and 34 years of age, with an elasticity of 0.2534% and for those between 55 and 64 years of age, with an elasticity of 0.0763%; and finally, a medium level of education, with an elasticity of 0.0175%. Yet some differences emerge for men and women. The average male entrepreneur has experience (elasticity = 0.15%) and is aged either 25-34 years (elasticity = 0.098%) or 55-64 years (elasticity = 0.097%); he also has earned secondary and medium education levels (elasticity = 0.0897%).
For female entrepreneurs, experience again has the greatest effect, with a similar elasticity (0.16%). However, the next most relevant characteristic is education, especially medium and superior levels (elasticity = 0.08%). Age also has an effect, such that the age interval from 35 to 44 years indicates an elasticity of 0.11% (elasticity of the variable TEAF3544), followed by 18 to 24 years, with an elasticity of 0.0689% (elasticity of the variable TEA1824), and then 25 to 34 years, with an elasticity of 0.0422% (elasticity of the variable TEA2534). Therefore, these findings suggest that entrepreneurial women, on average, decide to start business when they are between 18 and 44 years of age, which is opposite the results for men, who initiate their businesses either between 25 and 34 years or between 55 and 64 years of age.
To detect further differences in the models that may reflect country characteristics, the next model estimation classifies the sample according to the income level of the country. The results are in Table 5, and the associated elasticities for each of the variables, according to gender and income level of countries, are in Table 6.
The analyses in Tables 5 and 6 reveal that entrepreneurs in high-income countries, in average terms, have experience (elasticity = 0.556%) and range in age from 18 to 34 years (elasticity = 0.204%) or 55 to 64 years (elasticity = 0.0995%), with medium and superior educational levels (elasticity = 0.105%). In medium- and low-income countries, entrepreneurs again represent two main age groups with similar elasticities, namely, 55-64 years (elasticity = 0.199%) and 18-24 years (elasticity = is 0.196%). The characteristic that differentiates these two groups is experience; it is significant for the 55-64 year age group, with an elasticity of 0.545% (variable TEASUB), but not for the 18-24 year age group. That is, an older entrepreneur has more experience and initiates a new venture in response to an opportunity rather than need. The findings reverse for the youngest group of entrepreneurs, who lack experience and consequently create companies due to their need-based motives.
Although the level of education of the average entrepreneur in medium- and low-income countries is secondary or average, the latter predominates over former level, as reflected in the negative elasticity (-0.153% and -0.142%, pertaining to the variable TEASD) of an medium education level. Thus, as these countries improve their educational level, the number of entrepreneurs with a secondary level of education diminishes and the proportion with an average level increases, especially among young entrepreneurs. Accordingly, the elasticity of this variable TEAPS is high (0.196%) for the group of 18-24 year olds compared with that among the group of 55-64 years old (0.155%).
Furthermore, in high-income countries, the average male entrepreneur is between 25 and 34 (elasticity = 0.1341%) or 55 and 64 (elasticity = 0.0951%) years of age. Women instead predominantly represent the 35-44 year group (elasticity = 0.1266%), followed by 25 to 34 years (elasticity = 0.0636%) and 18 to 24 years (elasticity = 0.0409%). As for the men's educational level, the secondary-medium education level stands out (elasticity = 0.1147%), whereas female entrepreneurs exhibit a higher level of studies (elasticity = 0.1532%). This indicator may reveal that female entrepreneurs start their businesses later than men, apparently because they have obtained sufficient education. Finally, experience is a determinant factor for both men and women making the decision to begin a business. However, the variable has more weight for men than women, according to their respective elasticities (0.3121% versus 0.0750%).
These characteristics also differ for high- versus medium- and low-income countries. In the latter group of countries, the average male entrepreneur is younger (between 18 and 24 years, elasticity = 0.6305%), but older male entrepreneurs (55-64 years) in high-income countries show a higher elasticity (elasticity = 0.2622%). Women in both groups of countries reveal a significant elasticity only for the 18-24 year age group, though the elasticity is higher in medium- and low-income compared with high-income countries (0.1374%).
Finally, as for the educational level, in this group of countries, both male and female entrepreneurs possess an average educational level. The more influential factor for women is experience, whereas men indicate a negative elasticity for the experience variable (elasticity of -0.5196%). Men in these countries start businesses due to their need, not in response to opportunity, such that they sense an obligation to start a business despite lacking experience. The opposite effect occurs for women.
This study identifies sociodemographic factors that affect the entrepreneurship behavior of nascent and new entrepreneurs. Specifically, data pertaining to the characteristics of entrepreneurs from 22 countries in the GEM project reveal differences according to the gender of the entrepreneur and the socioeconomic level of considered country. The entrepreneurial characteristics with the most significant influences on entrepreneurship behavior are, in order, previous experience, age, and educational level.
Yet the results vary for different genders. When male entrepreneurs start a business, they tend to possess previous experience, have completed secondary studies, and represent average ages ranging primarily between 25 and 34 years or between 55 and 64 years. The average female entrepreneur has more education than her male counterpart and ranges in age mainly from 35 to 44 years (though women 18-24 and 25-34 years of age are also well represented). These results indicate that female entrepreneurs start their businesses later than men, apparently because at that point, she has obtained sufficient education. Yet the influence of experience is not that different on the behaviors of men versus women.
Both male and female entrepreneurs recognize the importance of previous experience for the success of their start-up project and work to detect and develop new business opportunities with innovative potential. Previous experience clearly helps potential entrepreneurs find innovative business opportunities and develop a network of contacts that can provide advantages for the creation and consolidation of their company. Such experience also may provide the entrepreneur with the confidence needed to initiate
This study also notes the effect of the level of country income, such that the characteristics of average entrepreneurs vary somewhat. The most relevant difference between high- and medium- and low-income countries for men relates to experience; older entrepreneurs have more experience than the youngest entrepreneurs, and older male entrepreneurs start their companies in sectors they know, so they can take advantage of business opportunities based on their previous experience. These results suggest though that regardless of the economic level of the country, a growing number of older workers resist definitive retirement and regard entrepreneurship as a means to remain active in the labor market. For women, country income reveals a greater age difference: In high income countries, female entrepreneurs are mostly 35-44 years, whereas in medium-and low-income countries, entrepreneurial women tend to be 18-24 years of age.
This model incorporates diverse variables related to the sociodemographic profiles of nascent and new entrepreneurs in 22 countries across four different continents and analyzes them according to both gender and the income per capita in their country. It therefore represents a key contribution to entrepreneurship research. Moreover, this study reveals the importance of sociodemographic variables in stimulating the creation and consolidation of companies.
Broadly, this research offers several contributions. First, in contrast with most prior investigations, which analyze entrepreneurs using samples from only one culture, this study considers a very wide sample from 22 countries that ensures great cultural diversity.
Second, this study identifies which sociodemographic characteristics of men and women at different socioeconomic levels have major impacts on their entrepreneurial behavior. Therefore, government agencies dedicated to increasing entrepreneurship rates in their countries can better determine which groups to focus on in their communication and recruitment efforts. Third, this investigation focuses on a very concrete phase of entrepreneurship, unlike previous work that fails to distinguish nascent, new, and consolidated entrepreneurs. By limiting the sample to nascent and new entrepreneurs whose companies have existed for less than 42 months, this study diminishes possible heuristic biases derived from the joint consideration of different phases in the creation of a company.
However, this study also suffers several limitations. In particular, the constructs in the causal model are not comprehensive; they constitute only a portion of the diverse precedents and consequences that could have been considered. However, to avoid excessive model complexity, these factors, precursors, and mediating variables have been limited purposefully. Further research should incorporate additional new variables in the model (e.g., public help programs, social networks, cultural values, business expectations). It also would be interesting to examine the type of planning that young versus older entrepreneurs undergo, the degree of innovation in the opportunities they detect, and the motivations that primarily stimulate them to create companies. For example, exactly what motivates men versus women of 55 to 64 years of age to create a company?
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Agustin Alvarez-Herranz (1), Pilar Valencia-De-Lara (2), Maria Pilar Martinez-Ruiz (3)
(1) University of Castilla-La Mancha, Area de Econometria, Facultad de Ciencias Economicas y Empresariales, Plaza de la Universidad, s/n, 02071 Albacete, Spain (2) University of Castilla-La Mancha, Area de Organizacion de Empresas, Facultad de Ciencias Sociales de Cuenca, Avenida de los Alfares, 44, 16071 Cuenca, Spain (3) University of Castilla-La Mancha, Area de Comercializacion e Investigacion de Mercados, Facultad de Ciencias Sociales de Cuenca, Avenida de los Alfares, 44, 16071 Cuenca, Spain E-mails: (1) Agustin.Alvarez@uclm.es; (2) Pilar. Valencia@uclm.es; (3) MariaPilar.Martinez@uclm.es (corresponding author)
Received 27 January 2011; accepted 6 April 2011
Agustin ALVAREZ-HERRANZ is Doctor in Economics and Business Administration. He is presently Associate Professor in the Department of Econometrics, University of Castilla-La Mancha, Spain. His research areas are mainly the study of entrepreneurship and the economics of transport and infrastructures. He has participated in different Conferences and Seminars worldwide and has written several articles and book chapters.
Pilar VALENCIA-DE LARA is Doctor in Economics and Business Administration. She is presently Associate Professor in the Department of Business Administration, University of Castilla-La Mancha, Spain. Her research areas are mainly: the study of entrepreneurship and the creation of a financial credit system with levels and jumps in the interest payments as it could apply to new business opportunities and to business development.
Maria Pilar MARTINEZ-RUIZ is Ph.D. in Economics and Business Administration, and currently Associate Professor at the Department of Marketing of the University of Castilla-La Mancha (Spain). She has participated in different Conferences and Seminars worldwide, has written several articles and numerous book chapters. Her main research lines are marketing communications, retailing, product and service innovation and entrepreneurship.
Table 1. Variables included in the general model Variable name Type Scale Description [TEA.sub.it] Endogenous Metric Percentage of population aged 18-64 years involved in launching a company (nascent entrepreneur) or owning a young company (new entrepreneurs) in country i at time t [4.summation over Exogenous Metric Level of education j of (j=1)] [EDUC.sub.jit] entrepreneurs in country i at time t Education variables TEASS Exogenous Metric Percentage of population aged 18-64 years involved in TEA with primary education TEASD Exogenous Metric Percentage of population aged 18-64 years involved in TEA with secondary education TEAPS Exogenous Metric Percentage of population aged 18-64 years involved in TEA with medium education over the total TEA TEAGE Exogenous Metric Percentage of population aged 18-64 years involved in TEA with superior education [2.summation over Exogenous Metric Percentage of (l=1)] [EXP.sub.jit] entrepreneurs with previous experience level l among all entrepreneurs in country i at time Experience variables TEAOWN Exogenous Metric Percentage of population aged 18-64 years involved in TEA answering "Yes" to the item: Currently owner-manager of a business asset TEASUB Exogenous Metric Percentage of population aged 18-64 years involved in TEA answering "Yes" to the item: Currently involved in start up of which I will own all or part [5.summation over Exogenous Metric Percentage of (s=1)] [EDAD.sub.sit] entrepreneurs in age category s of total entrepreneurs in country I at time t Age variables TEA1824 Exogenous Metric Percentage of population aged 18-24 involved in TEA TEA2534 Exogenous Metric Percentage of population aged 25-34 years involved in TEA TEA3544 Exogenous Metric Percentage of population aged 35-44 years involved in TEA TEA4554 Exogenous Metric Percentage of population 45-54 years involved in TEA TEA5564 Exogenous Metric Percentage of population aged 55-64 involved in TEA Notes: (1) TEA = Total Entrepreneurial Activity, an index designed to reflect the percentage of the adult population involved in nascent (less than 3 months) and new (3-42 months) companies (2) The education of primary level offers to the children basic competitions of reading, writing and mathematics, besides an elementary comprehension of topics as history, geography, natural sciences, social sciences, plastic arts and music. The education of secondary level completes the presentation of basic education that began in the primary level and points to lay the foundations for a learning and a human development that they last the whole life, offering an instruction more orientated to topics or specific competitions, with more specialized teachers. The tertiary, medium education or post secondary, though it does not represent the step to a qualification of advanced investigation, normally demands, as minimal requirement of admission, the satisfactory ending of the education of secondary level. The top education, where the sciences and the investigation would have major content, case of the diplomaturas, masters, masters, conferred a doctor's degree and post doctorates. Source: Institute of Statistics of the United Nations Organization for the Education, the Science and the Culture (UNESCO). Table 2. Variables included in the gender-based models Variable name Type Scale Description [TEAMAL.sub.it] Endogenous Metric Percentage of male population, 18-64 years of age, involved in launching a company or owning a young company in country i [TEAFEM.sub.it] Endogenous Metric Percentage of female population, 18-64 years of age, involved in launching a company or owning a young company in country i [5.summation over Exogenous Metric Percentage of male (s=1)] entrepreneurs in age category [EDADMAL.sub.sit] s of total entrepreneurs in country i at time t. The age range variables are the same as in the general model [5.summation over Exogenous Metric Percentage of female (s=1)] entrepreneurs in age category [EDADFEM.sub.sit] s of total entrepreneurs in country i at time t. The age range variables are the same as in the general model [4.summation over Exogenous Metric The level of education j of (j=1)] male entrepreneurs in country [EDUCMAL.sub.jit] i. The education variables are the same as in the general model [4.summation over Exogenous Metric The level of education j of (j=1)] female entrepreneurs in [EDUCFEM.sub.jit] country i. The education variables are the same as in the general model [2.summation over Exogenous Metric The percentage of male (l=1)] entrepreneurs with previous [EXPMAL.sub.jit] experience level l of total male entrepreneurs in country i. The experience variables are the same as in the general model [2.summation over Exogenous Metric The percentage of female (l=1)] entrepreneurs with prior [EXPFEM.sub.jit] experience level l of total female entrepreneurs in country i. The experience variables are the same as in the general model Table 3. Estimations obtained in the general, male, and female models: 2002-2006 Variable General Men Women Coefficient Coefficient Coefficient C 0.010054 0.04183 0.022318 t-Statistic 3.750508 9.913256 17.20055 TEA1824 0.037537 t-Statistic 3.62634 TEA2534 0.023301 0.005765 t-Statistic 8.743899 2.75243 TEA1834 0.036318 t-Statistic 3.192402 TEA3544 0.015138 t-Statistic 6.532628 TEA5564 0.050886 0.089149 t-Statistic 4.665341 5.784141 TEAPS 0.003584 t-Statistic 1.824434 TEAMSDPS 0.011137 t-Statistic 1.885498 TEAFPSGE 0.007497 t-Statistic 4.168436 TEASUB 0.048071 0.018046 0.012144 t-Statistic 7.880816 6.166281 2.401513 Unweighted Statistics R-squared 0.907486 0.838947 0.88261 Durbin-Watson stat 1.827488 2.425397 1.967734 Notes: Dependent Variable: TEA Method: Pooled EGLS (Cross-section weights) Sample: 2002 2006 Included observations: 5 Cross-sections included: 22 Total pool (balanced) observations: 110 Linear estimation after one-step weighting matrix White cross-section standard errors & covariance (d.f. corrected) Table 4. Average variable elasticities Model General Men Women TEA1824 0.0689 TEA2534 0.0979 0.0422 TEA1834 0.2534 TEAF3544 0.1142 TEA5564 0.0763 0.0973 TEAPS 0.0175 TEAMSDPS 0.0897 TEAFPSGE 0.0812 TEASUB 0.4818 0.1503 0.1578 Table 5. Estimation results, grouped according to country income level: 2002-2006 Variable General High Medium Medium income and low and low countries income income countries countries * C -0.00144 0.03085 0.02472 t-Statistic -0.33207 1.01342 0.96544 TEA1824 0.09935 t-Statistic 1.75394 TEA2534 t-Statistic TEA1834 0.03465 t-Statistic 3.21742 TEA3544 t-Statistic TEA5564 0.05009 0.31135 0.29582 t-Statistic 4.58333 2.64888 2.64598 TEASD -0.04566 -0.04941 t-Statistic -3.00381 -3.07464 TEAPS 0.11606 0.09221 t-Statistic 3.58969 2.31245 TEASDPS t-Statistic TEAPSGE 0.00974 t-Statistic 1.77474 TEASUB 0.04645 0.03365 0.08281 t-Statistic 6.69270 0.73809 2.31165 AR(1) t-Statistic Unweighted Statistics R-squared 0.8758 0.9250 0.9048 Durbin-Watson 2.5845 2.3823 2.1348 Variable Men Women High Medium High Medium income and low income and low countries income countries income countries countries C 0.01962 0.08519 0.01727 0.02735 t-Statistic 2.86677 10.92252 5.21388 1.84478 TEA1824 0.35566 0.02493 0.06476 t-Statistic 7.70617 3.47131 2.01510 TEA2534 0.02835 0.00657 t-Statistic 3.51063 1.68966 TEA1834 t-Statistic TEA3544 0.01196 t-Statistic 4.64558 TEA5564 0.06907 0.48991 t-Statistic 2.22485 11.32350 TEASD t-Statistic TEAPS 0.16869 0.11031 t-Statistic 12.82200 4.09883 TEASDPS 0.01134 t-Statistic 2.29989 TEAPSGE 0.00949 t-Statistic 5.94021 TEASUB 0.03614 -0.15617 0.00458 0.03862 t-Statistic 2.34219 -30.96547 0.59940 2.02263 AR(1) -0.47788 t-Statistic -5.07088 Unweighted Statistics R-squared 0.7705 0.9575 0.7514 0.8860 Durbin-Watson 3.0929 2.1014 2.5654 1.9931 Notes: (1) The third column under the general model features entrepreneurs aged between 55 and 64 years, for which the relevance of experience was hidden in the general model by the higher weight in the sample of entrepreneurs aged between 18 and 24 years Dependent Variable: TEA Method: Pooled EGLS (Cross-section weights) Sample: 2002 2006 Linear estimation after one-step weighting matrix White cross-section standard errors & covariance (d.f. corrected) Table 6. Average variable elasticities by country income level: 2002-2006 Variable General High Medium Medium income and low and low countries income income countries countries * TEA1824 0.196 TEA1834 0.204 TEA2534 TEA3544 TEA5564 0.0995 0.189 0.199 TEASD -0.153 -0.142 TEAPS 0.155 0.196 TEAMSDPS TEAPSGE 0.105 TEASUB 0.556 0.545 0.221 Variable Men Women High Medium High Medium income and low income and low countries income countries income countries countries TEA1824 0.6305 0.0409 0.1374 TEA1834 TEA2534 0.1341 0.0636 TEA3544 0.1266 TEA5564 0.0951 0.2622 TEASD TEAPS 0.2633 0.1845 TEAMSDPS 0.1147 TEAPSGE 0.1532 TEASUB 0.3460 -0.5196 0.0750 0.3121 Notes: (1) The third column under the general model features entrepreneurs aged between 55 and 64 years, for which the relevance of experience was hidden in the general model by the higher weight in the sample of entrepreneurs aged between 18 and 24 years Table 7. Hypotheses results Sample Hypothesis Total sample H1 supported of countries H2 rejected H3 supported Man sample H1 supported H2 rejected H3 supported Women sample H1 supported H2 supported H3 supported High income H1 supported countries sample H2 supported H3 supported Man sample H1 supported H2 rejected H3 supported Women sample H1 supported H2 supported H3 supported Medium and low H1 supported income countries H2 rejected sample H3 supported Man sample H1 supported H2 rejected H3 rejected Woman sample H1 supported H2 rejected H3 supported
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|Author:||Alvarez-Herranz, Agustin; Valencia-De-Lara, Pilar; Martinez-Ruiz, Maria Pilar|
|Publication:||Journal of Business Economics and Management|
|Date:||Sep 1, 2011|
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