High Growth Aspiration and Female Entrepreneurship in the UK: unravelling the effects of gender from business characteristics
Mark Hart1*; Aloña Martiarena1; Jonathan Levie2 and Michael Anyadike-Danes3 1
Economics and Strategy Group, Aston Business School, UK
2
Hunter Centre of Entrepreneurship, University of Strathclyde, UK
3
Economic Research Institute of Northern Ireland, UK
*Corresponding author: Mark Hart, Economics and Strategy Group, Aston Business School, Aston Triangle Birmingham, B4 7ET, UK; tel: (+44) 121 204 3048; fax: (+44) 121 204 3036; email:
[email protected].
High Growth Aspiration and Female Entrepreneurship in the UK: unravelling the effects of gender from business characteristics
Abstract Recent policy initiatives in the UK have been designed to increase the number of growth-oriented women-led businesses. In keeping with much of the academic literature, there would appear to be an emerging proposition that there is a general aversion by female entrepreneurs to debt finance with few accessing external finance in either starting up in business or for any growth plans which they might have. Potential undercapitalization of these businesses at start-up is believed to have significant implications on the future growth of women-led businesses. Further, some avoided growth because they did not want to take on debt. This paper builds on that research and shows that although there are no direct gender effects in a model of high growth entrepreneurship there are indirect effects as captured by an interaction term of gender and start-up capital.
INTRODUCTION High Growth Firms (HGFs) have attracted considerable attention from the academic and policy community in recent years (Henriksson and Davidsson, 2009; Anyadike-Danes et al., 2009; BERR, 2008). Whilst there has been a great deal of research on the characteristics of high growth firms using business demography datasets in terms of firm size, industrial sector, business age and location, less attention has been paid to the relative importance of the range of individual characteristics associated with the high growth aspiration of new firms. So, while we are reasonably well informed about the types of businesses who can be classified as high growth, we are perhaps less able to identify the extent to which we can build a profile of those individuals most likely to set up enterprises which aspire to rapid growth. The main objective of the paper is to model the likelihood of an individual reporting that they aspire to high growth as defined by the number of jobs they intend to create after 5 years. We do this through an econometric investigation of the Global Entrepreneurship Monitor (GEM) UK 1
datasets for 2002-08 which include data on around 160,000 individuals aged between 18 and 64 years. Of particular interest is to examine the role of gender in understanding the growth propensity of new ventures.
From the perspective of public policy there has been a number of recent policy initiatives in the UK designed to increase the number of growth-oriented women-led businesses. For example, the Strategic Framework for Women‟s Enterprise (2003) advocated a collective long-term approach to the development of women‟s enterprise in order to “significantly increase the numbers of women starting and growing businesses in the UK, to proportionately match or exceed the level achieved in the USA”. More recently, the UK Women‟s Enterprise Task Force report on “Greater Return on Women‟s Enterprise – GROWE” again emphasised the importance of creating opportunities to increase the quantity, scalability and success of women‟s enterprise (WETF, 2009).
Connected to this, and in keeping with much of the academic literature, there would appear to be an emerging proposition that there is a general aversion by female entrepreneurs to debt finance with few accessing external finance in either starting up in business or for any growth plans which they might have. Potential undercapitalization of these businesses at start-up is believed to have significant implications on the future growth of women-led businesses. Further, some avoided growth because they did not want to take on debt. This paper builds on that research.
2
GENDER AND NEW FIRM GROWTH
Much has been written in recent years concerning the gender gap in entrepreneurial activity (see, for example, Allen et al., 2007). However, much of that research has concentrated on differences in the levels of start-up activity between men and women and little on an investigation of the growth aspiration of new ventures led by men and women. Almost all the academic literature reflects the fact that “women are not a homogenous group”. It is, therefore, important to recognize that there is no one group of women, no one group of female entrepreneurs and equally no one group of “growth orientated” female entrepreneurs. Gundry and Welsch (2001) highlighted the range of strategic paths chosen by women entrepreneurs and the extent to which some of them led to the growth of the business. The key conclusion from their study of over 800 women entrepreneurs across a range of sectors was that high-growth oriented women entrepreneurs adopted a more structured approach to organising their business with a key emphasis upon market and technological change, a teambased form of organisational design.
Keppler and Shane (2007) pose the question “Are male and female entrepreneurs really that different?” Using data from the United States Panel Study of Entrepreneurial Dynamics (PSED) they conclude that, although there is evidence of gender difference on various aspects of entrepreneurial activity and behaviour (e.g., opportunity identification; motivations for start-up and the propensity to start low risk/low return ventures), there were no significant gender differences with respect to firm performance. This paper returns to that research question using the GEM datasets for the UK.
Qualitative research on the growth orientation of women-led businesses in one English region (East of England) of the UK reached the following set of conclusions (Hart and O‟Reilly,
3
2007). First, previous labour market experience is critical in shaping the businesses which women establish. Career history is vital in providing female entrepreneurs with the skills, networks and confidence to start up a business.
It also has a crucial part to play in
determining the type of businesses established by women and strongly linked to this the “high growth” potential that they might achieve. Second, for many female entrepreneurs the motivations to start up in business are inextricably linked to “the family”. The flexibility that business ownership affords is a key driver in this decision. However, these personal factors have a strong influence on the ability of such businesses to be sustainable and grow. Third, it has often been argued that female entrepreneurs tend to locate themselves in sectors where growth potential can be limited. While this is largely the case for the East of England and nationally, our research has demonstrated that there is a stock of large, women-led businesses in the East of England and Hertfordshire, operating across the production and service sectors, making an important contribution to the regional economy and demonstrating significant potential for growth. Fourth, many female entrepreneurs believe that their business has “high growth” potential as opposed to being “high growth”. However, few of these entrepreneurs have any formalized plans for growth.
Fifth, only a small number of the female
entrepreneurs felt that their gender had a detrimental effect on these aspects of the business. Finally, in keeping with much of the academic literature, there was a general aversion by the female entrepreneurs to use debt finance. Most of the entrepreneurs were uncomfortable with debt and few accessed external finance in either starting up in business or for any growth plans which they might have. Potential undercapitalization of these businesses at start-up is believed to have significant implications on the future growth of women-led businesses. Further, some avoided growth because they did not want to take on debt.
A detailed quantitative investigation of the direct and indirect effects of gender on financial capital was undertaken by Verheul and Thurik (2001) using data on start-ups from the 4
Netherlands. They paid particular attention to both the amount of start-up capital and its composition (i.e., a simple distinction between equity and debt). Their main conclusion was that female entrepreneurs reported smaller amounts of start-up capital than their male counterparts, and when corrected for a range of other characteristics (e.g., type of business; experience; time engaged in networking) this was still the case pointing to a direct effect of gender rather than one mediated by the „female profile‟ of entrepreneurs in the panel of startups. Connecting the size of start-up capital and the subsequent growth performance of the business is an important dimension of understanding how gender impacts on business performance. However, the study made no attempt to relate the results to a comparison of the growth performance of businesses led by men and women. So, while we see from the Verheul and Thurik study that gender matters in terms of start-up capital we are still left with the question does gender matter in an explanation of firm growth?
THEORETICAL PERSPECTIVES
We are interested here in developing an important gender dimension to the theoretical framework which seeks to explain the growth of firms in their early years, namely the individual characteristics of the founder or founders of the business. These might include other demographic variables (e.g., age and ethnicity), attribute variables (e.g., education, household income) and cognitive variables which seek to capture aspects of experience, skills, motivations and aspirations.
Previous empirical research indicates that the entrepreneurial attributes most likely to influence the growth trajectory of new start-up businesses are a combination of motivation, work skills and information (e.g., Barkham, 1994; Birley and Westhead, 2004; Barkham et 5
al., 1996). However, one of the criticisms of much of this previous research is set out by Delmar and Davidsson (2006) who argue that many of the studies are retrospective once the business has been launched and suffer from the twin weaknesses of „hindsight biases‟ (poor recollection of what actually happened) and positive selection biases (data available on only those up and running a business). Their own longitudinal research on Sweden focused on the firm size expectations of nascent entrepreneurs and reported that almost two-thirds indicated that they did not expect to employ more than one employee after 5 years. They conclude that the initial size at start-up was a major explanatory factor in their models of expected future size but other factors that did improve the predictive capacity of the model related to aspects of the „commitment‟ of the founder(s) to the business – that is, formal legal incorporation of the business, dependency on the business as a main source of income, and growth as an explicit goal.
Whilst we do not have a longitudinal sample in the GEM UK dataset we are, however, able to develop the empirical research along the lines proposed by Delmar and Davidsson (2006) by examining the future size expectations of nascent and new business owners.
Previous
research using the GEM Global dataset (2000-06) has found that high-expectation and highgrowth entrepreneurs represent only a small percentage of all entrepreneurial activity (Autio, 2007). Even though 12.3 per cent of the adult-age population in countries that participated in the GEM study are active in emerging and new entrepreneurial businesses, only 6.5 per cent of new entrepreneurs (owner-managers of entrepreneurial firms less than 42 months old) expected to have 20 or more jobs in five years‟ time.
Even though high-expectation
entrepreneurship (as defined in this way) is rare, its contribution to expected job creation is important. Nascent and new entrepreneurs expecting to create more than 100 jobs in five years represent only 1.7 per cent of all nascent and new entrepreneurs, yet they expect to create nearly 50 per cent of all expected jobs. Almost 90 per cent of all expected new jobs are 6
foreseen by less than one-quarter of nascent and new entrepreneurs (Autio, 2007). The analysis of the individual characteristics revealed that education and household income, as well as entrepreneurial activities and attitudes, appear important for high-expectation and high-growth entrepreneurship. High-expectation and high-growth entrepreneurs are better educated (i.e., graduates) than other entrepreneurs and the general population.
High-
expectation and high-growth entrepreneurs are also likely to be wealthier than other entrepreneurs and the general population.
What we seek to do in this paper is to develop a framework which argues that the degree of risk aversion associated with growing a business is related to a bundle of individual characteristics which mediate the probability of aspiring to growth. We model ambition as a function of the quality of founder‟s general and specific human capital (Becker, 1975), social capital and financial capital. We suggest that founder‟s general human capital raises the opportunity cost of entrepreneurship versus other occupations (Evans and Jovanovic, 1989). Thus, founders with superior general human capital, such as graduate education and high income, will choose entrepreneurship only if it offers superior intellectual and financial rewards – that is, faster growth. Founders with superior financial capital will be more able to leverage the funds required to grow a business quickly.
Of central interest in this paper is to make some contribution to the debate on the effects of gender in models of high growth entrepreneurship.
In particular, we are seeking to
understand the relative importance of direct and indirect gender effects.
7
DATA
For the empirical analysis we used the pooled Global Entrepreneurship Monitor (GEM) dataset from the UK covering the period 2002-2008. The distinctive feature of this survey is that it provides information at both the business level and the individual entrepreneur level. Data is collected through a random telephone survey to the whole adult population, which allows controlling for selection bias. Thy final sample is comprised by 123,420 observations, from where 2,593 are classified as entrepreneurs1. In this case, entrepreneurs are considered as those who are currently involved in the setting up of a business or own and manage a new business (younger than 3.5 years).
For the dependent growth variable, we use the GEM question which asks nascent entrepreneurs and new business owners “Approximately how many people will be working for this business, not counting the owners but including all exclusive subcontractors, when it is five years old?” We use the answer – the expected number of employees - as a measure of entrepreneurial growth ambition and define a dummy variable called „high growth aspiration entrepreneurs‟ for those who expect to hire more than 20 employees in the next five years.
The independent variables used in the analysis are as follows (Table 1). We are naturally constrained by the range of variables collected as part of the GEM UK survey and they fall into two broad groups: individual level variables and firm level variables. All the included individual-level variables reflect current conceptual thinking on the possible range of demographic factors that determine the likelihood of an individual engaging in an entrepreneurial act (see, for example, Levie, 2007).
1
Due to a high presence of missing values among entrepreneurs their percentage among the adult population is lower than in the original sample.
8
Besides gender, which is obviously the focus of this paper, the other individual level variables include other demographics or personal indices (age, ethnicity and migrant status). Ethnicity and mobility are an important dimension of entrepreneurial behaviour and, a priori, we would expect certain ethnic groups and mobile individuals to be more likely to report higher growth aspirations. There are also variables which are objective measures of general human capital, such as education while other human capital variables are more specific about engagement in entrepreneurial behaviour, such as having invested in someone else‟s new business in the past three years, or having shut a business in the last 12 months. There is also a measure of financial capital (household income) which may reflect the ease with which higher income households (in this case greater than £50,000) are able to raise the necessary start-up capital for a faster growing business but it may also reflect a greater tolerance to the potential risk associated with growth.
There are three firm level variables included in the analysis. First, we include the sector of the start-up in order to reflect the observation that male and female entrepreneurs tend to work in different sectors of the economy. For example, female entrepreneurs are more likely to be engaged in new venture creation in retail and service sectors. Second, a variable is included to capture the extent to which the nascent or new business owner is using new technology (i.e., technology which was not in widespread use 12 months previously). This is designed to control for the effects of new technology on growth as we seek to isolate direct gender effects. Third, we include data on the amount of start-up capital required for the startup to ensure that any gender differences, as suggested by Verheul and Thurik (2001), are controlled for in the growth model. Finally, we insert a spatial dummy to control for the higher propensity of high growth businesses in London (Anyadike-Danes et al., 2009).
9
Data on start-up capital in the GEM dataset was highly skewed, so in order to control for outliers we followed the Winsor technique: that is, the highest start-up capital values were truncated at the 99% percentile value. The same procedure was applied for the expected number of employees (the dependent variable), but in this case 5% of outlier cases were assigned the truncation value.
METHOD
We argue that there is a selection issue that needs to be addressed before the analysis of growth can be undertaken. In brief, we know that women are significantly less likely than men to be nascent or new business owners and the GEM international data consistently confirms this (Allen et al., 2007; Wagner, 2007). So, before we run the growth regression (which refers to the likelihood of having high growth orientation) we need to control for the different ways women and men perceive the net benefits of running their own business compared to the net benefits of remaining in paid employment, - in other words the likelihood of becoming an entrepreneur. It is rare for this selection issue (i.e., choosing new venture creation) to be addressed in studies of gender and entrepreneurial activity and behaviour where the analysis is usually carried out on a sample of entrepreneurs but we have an opportunity to do so here. We use a Heckman selection model or a probit model with sample selection which requires the inclusion of one variable in the selection equation (i.e. being an entrepreneur) which is not, a priori, associated with growth and, therefore, can be excluded from the main equation. Previous research has pointed to the increased likelihood of an individual entering entrepreneurship if they have previously engaged in entrepreneurial activity. For this reason, we expect to find that closing a business in the last 12 months will
10
increase their propensity of an individual to start a new business, and much of the literature on serial entrepreneurs would support that assertion (Westhead et al., 2005). By contrast, we do not find any theoretical foundation to expect any correlation between high growth entrepreneurial entry and a recently „exited‟ entrepreneurial experience. This exclusion restriction was successively tested by running the second stage equation and adding this variable which was statistically insignificant.
RESULTS
The probit models with sample selection (Heckman) are presented in Table 2. As said, we are running a two stage Heckman specification. The first stage is a choice model – are you in the group or not? In this case, is the respondent an entrepreneur or not and we use predictors to determine this. The second stage then examines the effects of the independent variables on the outcome (high growth as measured by the expected number of jobs in 5 years exceeding 20 employees).
Each stage has a residual for each observation, or a set of unknowns for each observation. To test for bias, we examine the relationship between the residuals for the two stages (stage 1 and stage 2). If the unobservables in the selection model are correlated with the unobservables in the stage 2 model, we have biased estimates without selection correction (or in an OLS model). This is basically saying that unobservables in the selection (or choice) of becoming an entrepreneur are also affecting the stage 2 model. If the unobservables in stage 1 are unrelated to the unobservables in stage 2, then we are saying that stage 1 does not affect stage 2 results. This is another way of saying that selection into the sample of stage 2 is a
11
random process, unaffected by different unobservables. If we can pick all the right variables for our models, and leave few unobservable variables that affect our outcome, then chances are good that we will not have selection bias.
When rho is negative, as it is in this case, this indicates that unobservables in both stages are negatively correlated with one another and therefore, we can conclude that the fact that men are significantly more likely to be entrepreneurs compared to women (the female variable is negative and significant) does not affect the results of the growth model.
Having dealt with the issue of selection bias we can now turn our attention to the growth model. There are two models presented in Table 2 with the second model including an additional variable which is an interaction term – gender and start-up capital.
This is
included in order to understand more clearly the direct and indirect effects of gender in the growth model. However, let us deal with the initial specification first and examine the results for gender. In Model 1 the effect of gender on growth is negative but insignificant when predicting the entry into high growth oriented entrepreneurship. In other words, women are no less likely than men to be engaged in high growth business ventures after we control for the sector in which they are setting up their business, the amount of start-up capital and a bundle of individual level variables.
Start-up capital is positively and significantly associated with growth oriented start-ups. Looking closely at these other „control‟ variables we note that personal financial capital (household income) is positive and significant. Although we are not able to control for sources of funding and, therefore, test whether women entrepreneurs really do rely on close informal investors (i.e., family and friends) as is usually argued (see, for example, Verheul
12
and Thurik, 2001; Coleman 2009), we find a positive relationship between wealth and high growth orientation. We include highest reported household income category, as we expect stronger effects of wealth among the highest percentiles (Hurst and Lusardi, 2004). The age of the entrepreneur is also an important predictor of the likelihood to aspire to high growth – in general, high growth is more prevalent amongst younger adults. There are two other behavioural variables of note in the model. Having made informal investments in other businesses is positively and significantly related to high growth entrepreneurship as is the use of new technology in the business venture.
Turning our attention to Model 2 we now attempt to examine the direct and indirect effects of gender in the growth model (Table 2). Interestingly, when we include the interaction term between female and start-up capital the single direct effect of gender reversed and is now positive (but again insignificant). However, the interaction term is negative and significant and now adds to the other significant variables observed in Model 1 to contribute to an overall prediction of high growth expectation. What this suggests is that the lower amount of start-up capital in female-led start-ups explains the negative effect of gender captured in the initial specification (Model 1). This is in line with Fairlie and Robb (2009) who find that the lower levels of start-up capital employed in female-owned businesses is associated with the underperformance of their ventures.
One final modification to the analysis is undertaken. Given the arbitrary cut-off point for high growth entrepreneurial classification, as we defined for entrepreneurs expecting to hire more than 20 employees, we relax this criterion and look at the exact number given by the respondents. In this case, we run the negative binomial specification (Table 3). The results corroborate the previous finding that women entrepreneurs do not, a priori, create less
13
growth oriented businesses, but that the undercapitalisation of their businesses may constrain their growth.
CONCLUSIONS
The analysis of the GEM UK pooled dataset for 2002-08 has shown that, after controlling for selection into entrepreneurship in the first place, there are no significant direct gender effects in a model to explain high growth entrepreneurship. We observe that there is a significant and positive effect of the amount of start-up capital on the outcome of high growth entrepreneurship. The significance and sign of the interaction term between gender and startup capital suggests that there are indirect effects of gender in the growth model. In brief, women entrepreneurs start their new business venture with less capital than men and this has a negative effect on high growth entrepreneurship.
14
Table 1: Descriptive Statistics Mean
SD
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Firm level variables Expected no. Employees Female Start-up capital Female*su capital Mining, construction Manufacturing
28.38 0.58 9.41 3.25 0.10 0.07
Utilisation, transport, 7 communications 8 Wholesale trade
0.04 0.05
0.19 0.22
Retail trade, 9 hotels & restaurants
0.20
0.40
Fin. intermediation, 10 real estate activities 11 Business services
0.05 0.23
0.22 .00 -.06* .12* .01 -.08* -.07* -.05* -.05* -.12* 0.42 .06* -.03 -.05* -.06* -.18* -.15* -.11* -.13* -.28* -.13*
Govt, health, education, 12 social services
0.11
0.32
-.01
0.11 0.01
0.32 0.07
-.02 .12* -.12* .04 -.03* .03
1 2 3 4 5 6
13 14 15 16 17 18 19 20 21 22
Consumer service activities New technology Individual level variables Age Age sq Graduate White In-migrant High Income London Informal Inv.
43.25 2012.30 0.30 0.96 0.42 0.03 0.03 0.01
244.80 0.49 -.03 2.14 .06* -.21* 4.46 -.01 .96* 0.30 -.02 -.18* 0.26 .00 -.02 -.01 -.04* -.01 -.09* .00
.07*
-.02 .01 -.14* .01 -.03 -.09* .03 -.03 -.06* -.05* .04 -.06* -.08* -.06* -.05* .08*
.16* -.09*
11.92 .02 -.04* .08* 1024.93 .02 -.04* .07* 0.46 .00 -.02* .05* 0.20 .00 .01* -.05* 0.49 .01 .02* -.02 0.16 .05* -.03* .16* 0.18 -.01 .00 .04 0.11 .02 -.04* .10*
.13* -.16* -.14* -.10* -.12*
.09* -.12* -.10* -.07* -.08* -.18* -.08* -0.2* .03 -.12* -.10* -.07* -.08* -.18* -.08* -0.2* -.13* .02 -.02 .02 .02 .02 -.03 -.01 0.04 .00 .02 .00 .06* -.03 .03 .06* .02 .00
-.06* -.06* -.16* .05* -.08* -.04 -.03 -.01
.05* .03 -.05* -.03 .05* .02 -.04* -.03 .02 -.05* -.06* -.05* .01 .02 -.02 -.06* .01 .01 -.04* -.02 .01 -.02 -.01 -.03 -.01 -.03 .00 -.01 .00 .01 .03 .01
n= 123,420. * Correlations significant at the 0.05 level.
15
.01 .01 .03 -.02 .05* .07* .01 .02
.05* .05* .15* .02 .04 .03 .01 -.02
-.01
.01 -.04* -.01* .01 -.04* -.01* .99* .07* .00 .01* -.1* -.10* -.02 .00 -.01* .12* .12* -.07* .01 .00 .01* .11* .11* .16* .10* .00 .01 .02* .00 .00 .12* .00 .05* .03 .02 .01* -.05* -.05* .07* -.21* -.01* .04* -.03 -.01 .04* .00 .00 .03* -.02* .02* .06* .02*
Table 2: Selection Models (Heckman) of Growth Expectation
Female Age Age sq Graduate White In-migrant High Income London Shut business _cons rho Start-up capital Female*su capital Mining, construction Manufacturing Utilisation, transport, communications Wholesale trade Retail trade, hotels & restaurants Fin. intermediation, real estate activities Business services Govt, health, education, social services Consumer service activities New technology Informal Investor
(1)
(2)
Selection Equation Main Equation (y=entrepreneurship) (y=high growth ent.) -0.37*** (0.02) -0.17 (0.10) 0.03*** (0.01) -0.04* (0.02) 0.00*** (0.00) 0.00* (0.00) 0.15*** (0.02) 0.09 (0.07) -0.29*** (0.03) -0.15 (0.12) 0.19*** (0.02) 0.06 (0.08) 0.20*** (0.04) 0.33*** (0.12) 0.07* (0.04) 0.22 (0.14) 0.73*** (0.04) -2.15*** (0.10) -2.24*** (0.76) -0.30* (0.16)
Selection Equation Main Equation (y=entrepreneurship) (y=high growth ent.) -0.37*** (0.02) 0.64 (0.43) 0.03*** (0.01) -0.04* (0.02) 0.00*** (0.00) 0.00* (0.00) 0.15*** (0.02) 0.09 (0.07) -0.29*** (0.03) -0.16 (0.12) 0.19*** (0.02) 0.06 (0.08) 0.20*** (0.04) 0.33*** (0.12) 0.07* (0.04) 0.22 (0.14) 0.73*** (0.04) -2.15*** (0.10) -2.46*** (0.78) -0.29*** (0.16)
0.18*** (0.02) 0.83** (0.33) 0.87*** (0.33)
0.21*** -0.08* 0.82** 0.84**
(0.03) (0.04) (0.33) (0.34)
1.01*** (0.35) 0.81** (0.34)
0.98*** (0.35) 0.79** (0.35)
0.66** (0.32)
0.65** (0.32)
0.72** (0.33) 0.99*** (0.32)
0.69** (0.34) 0.97*** (0.32)
0.64* (0.33)
0.63* (0.33)
0.75** (0.33) 0.24*** (0.08) 0.24** (0.12)
0.72** (0.34) 0.24*** (0.09) 0.22* (0.12)
Wald chi2 (rho=0) 2.9 p-value= .0887 2.74 p-value= .0978 No. of obs 123,420 123,420 Censored obs 120,827 120,827 Uncensored obs 2,593 2,593 Note: ***significant at 0.01; ** significant at 0.05; * significant at 0.1. Robust s.e. in parentheses. The industry comprised by agriculture, fishing, forestry and hunting is omitted from the regression.
16
Table 2: Model of Growth Expectation - Negative Binomial estimates Female Start-up capital Female*su capital Mining, construction Manufacturing Utilisation, transport, communications Wholesale trade Retail trade, hotels & restaurants Fin. intermediation, real estate activities Business services Govt, health, education, social services Consumer service activities New technology Age Age sq Graduate White In-migrant High Income London Informal Investor _cons No. of obs LR chi2(21)
Coeff. 0.57* 0.14*** -0.13*** 1.08*** 1.52***
s.e. (0.30) (0.02) (0.03) (0.25) (0.26)
1.26*** 0.92***
(0.29) (0.27)
1.72***
(0.23)
1.37*** 2.48***
(0.27) (0.23)
1.26***
(0.25)
1.22*** 1.09*** -0.01 0.00 0.26*** -0.09 -0.09 0.67*** 0.04 0.88*** -0.08
(0.25) (0.13) (0.03) (0.00) (0.08) (0.14) (0.09) (0.17) (0.18) (0.18) (0.59)
2,332 551.14
Note: ***significant at 0.001; ** significant at 0.05; * significant at 0.1. Robust s.e. in parentheses. The industry comprised by agriculture, fishing, forestry and hunting is omitted from the regression.
17
REFERENCES Allen, IE; Elam, A; Langowitz, N & Dean, M (2007)
Report on Women and
Entrepreneurship - 2007, Global Entrepreneurship Monitor Anyadike-Danes, M., Bonner, K., Hart, M., & Mason, C.M. (2009). Mapping Firm Growth in the UK: The Economic Impact of High Growth Firms, NESTA, London. Autio, E. (2007). GEM 2007 Report on High‐Growth Entrepreneurship, GEM Global Reports. London: GERA. BERR (2008). High Growth Firms in the UK: lessons from an analysis of comparative UK performance, BERR Economics Paper No. 3, November 2008. Barkham, R (1994). Entrepreneurial Characteristics and the Size of the New Firm: A Model and an Econometric Test, Small Business Economics, 6 (2), 117-125. Barkham, R ; Gudgin, G; Hart, M & Hanvey, E (1996) The Determinants of Small Firm Growth: and inter-regional study in the UK, 1986-90, Jessica Kingsley: London. Becker, G S (1975) Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education, 2nd ed National Bureau of Economic Research. Birley, S. & Westhead, P. (1994). A Taxonomy of Business Start-up Reasons and their Impact on Firm Growth and Size, Journal of Business Venturing, 9 (1), 7-31. Coleman, S., &
Robb, A. (2009). A Comparison of New Firm Financing by Gender:
Evidence from the Kauffman Firm Survey Data, Small Business Economics, 33 (4), 397-411. Davidsson, P & Delmar, F. (2003). High growth firms and their contribution to employment: the case of Sweden. In Davidsson, P, Delmar, F and Wiklund, J (eds) Entrepreneurship and the Growth of Firms, Edward Elgar: Cheltenham, pp 156-178. 18
Evans, D. S., & Jovanovic, B. (1989). An Estimated Model of Entrepreneurial Choice under Liquidity Constraints. The Journal of Political Economy, 97(4), 808-827. Gundry, L.K., & Welsch, H.P. (2001). The Ambitious Entrepreneur: High Growth Strategies of Women-Owned Enterprises, Journal of Business Venturing, 16 (5), 453-470. Fairlie, R., & Robb, A. (2009). Gender Differences in Business Performance: Evidence from the Characteristics of Business Owners Survey, Small Business Economics, 33 (4), 375-395. Hart, M & O‟Reilly, M (2007) High-Growth Female Entrepreneurs in the East of England, Research Report for the East of England Development Agency (mimeo available from author) Henrekson, M. & Johansson, D. (2008). Gazelles as job creators: a survey and interpretation of the evidence, Working Paper 733, Research Institute of Industrial Economics, Stockholm Henrekson, M and Johansson, D (2009) Gazelles as job creators: a survey and interpretation of the evidence, Small Business Economics, February (DOI 10.1007/s11187-009-9172z) Hurst, E., & Lusardi, A. (2004). Liquidity Constraints, Household Wealth, and Entrepreneurship. Journal of Political Economy, 112(2), 319-347. Keppler & Shane (2007) Are Male and Female Entrepreneurs Really that Different? SBA Working Paper 309. Levie, J. (2007). Immigration, In-Migration, Ethnicity and Entrepreneurship in the United Kingdom, Small Business Economics, 28 (2), 143-169. Verheul, I., & Thurik, R. (2001). Start-Up Capital: Does Gender Matter?, Small Business Economics, 16 (4), 329-346. 19
Wagner, J (2007) What a Difference a Y makes – Female and Male Nascent Entrepreneurs in Germany, Small Business Economics, 28, 1-21. Westhead, P., Ucbasaran, D. & Wright, M. (2005). Experience and Cognition: Do Novice, Serial and Portfolio Entrepreneurs Differ? International Small Business Journal 23(1): 72-98. Women‟s Enterprise Task Force (2009) Greater Return on Women’s Enterprise – GROWE, WETF, UK
20
