5 Self-employment and gender
5.1 Introduction
As mentioned in Chapter 2, the prevalence of women-owned businesses varies significantly across countries (Minnitiet al., 2004). In the USA nearly half of all privately owned firms (48 per cent) were women-owned in 2004 (Center for Women’s Business Research, 2005). In Canada women constitute one-third of the total number of self-employed Canadians (CIBC, 2005). The prevalence of women-owned businesses in Finland is comparable to that of Canada. In Finland, 33 per cent of those 304,000 persons who were classified as self- employed in the 2003 Labour Force Survey were women (Arenius and Autio, 2006). Also, based on the data generated from the GEM (Global Entrepreneur- ship Monitor) database in 2002, the UK was found to rank 23rd out of 37 coun- tries in terms of the ratio of female-to male-owned businesses. In 2003, with respect to the gap in the rate of venture creation by men and women, the UK ranked 7th out of 14 participating G7 and EU countries (Hardinget al., 2004).
However high the growth rates are, the level of female entrepreneurship is still low. These low levels of involvement of women and individuals from a minority background into entrepreneurship in the UK is behind the perception at govern- ment level that there are still substantial segments of the population that could benefit from being self-employed but that still do not take advantage of this option because of the substantial entry barriers, mostly of a financial nature. In England a wide consultation exercise by the Small Business Support (SBS) unit (2003a) led to the launch of a “Strategic Framework for Women’s Enterprise”
(SBS, 2003b), in collaboration with the devolved administrations, various government departments, and with Prowess, a national UK network to promote women’s enterprise. The strategic framework has identified six barriers to women’s greater participation in entrepreneurial activity (SBS, 2003b, page 8):
lack of appropriate business support; lack of access to finance; the impact of caring and domestic responsibilities; difficulties experienced in the transition from benefits to self-employment; lack of appropriate role models; and low levels of confidence and self-esteem.
However, the previous research in this area (as summarized in Chapter 2) has emphasized the complexity of the issues related to the finance of women-led
companies and particularly the difficulty of trying to isolate and characterize any specific gender effect. Is it the case, for example, that lending institutions discrimi- nate either deliberately or unwittingly against female entrepreneurs? Or, are female entrepreneurs simply more reluctant to seek external finance for their companies?
Other factors linked to background, experience or even sector to which the company belongs may also be important in shaping males’ and females’ access to finance. So who is right and who is wrong? Are women financially discriminated against or do women self-select themselves and simply do not apply for external funding? These two opposite views are based on two different assumptions on how access to external funding first and then the self-employment choice are influenced by both gender and ethnicity. In the first case, it is assumed that financial institu- tions ration credit based on either gender or ethnic background, variables that are thought by external funders (in a presumably rather unconscious way) to be able to proxy for the capability the individual has of running a company successfully: in this case, the probability that individuals have of experiencing financial constraints is influenced directly by both the gender and its ethnic background; then only those that are not financially constrained can choose to become entrepreneurs. In the second view, the relationship between gender, ethnicity and financial constraints is much subtler than predicted by the first view: indeed, in this case it is possible that both women and individuals from a disadvantaged ethnic background expect to meet substantial future financial constraints when trying to have access to external funding to set up their own entrepreneurial activity. In other words, they may expect to be both credit rationed and to have unfavourable credit conditions. This may not necessarily mean they will be financially constrained once they apply for external funding; it simply means that based on the observation of the aggregate variables (in this case the small proportion of women and individuals from minor- ity backgrounds having access to external credit) they expect to be credit-rationed based, indeed, on both their gender and ethnic background. This is possible because female applicants expect the terms of the loan agreement to be in favour of the financial institutions and therefore they are aware that most of the generated surplus will be appropriated (entirely or mostly) by the lender. So women prefer not to apply for external funding and therefore what is observed in the aggregate is the small proportion of loans granted to female applicants.
So a self-selection mechanism is at work where the probability an applicant has of approaching external funders is affected by both its gender and its ethnic background. This way, the self-employment choice is conditioned by either/both gender or/and ethnic background through the probability of approaching external funders. This last mechanism is consistent with the notion that economic agents may decide not to participate in a financial relationship if they expect that most of the surplus generated by them will be appropriated by the financial institution. In other words, economic agents internalize the impact of future financial con- straints and this way, finance constraints affect the observed economic outcomes.
Obviously it is possible that in reality a mix of the two mechanisms (namely, women do not get external funding and women do not seek external finance) can be at work and may contribute to explain the pattern that is observed in the data.
However, understanding which view prevails in reality is quite important as the policy interventions required for the two cases differ substantially. While in the first case, policy must be focused on removing the barriers to credit access, in the second case, policy-makers have to make sure that the perception of how financial intermediaries work among potential applicants changes among the potential applicants.
The purpose of this chapter is to analyse the relationship between financial con- straints, gender, ethnic background and the individual’s self-employment choice1 using English data drawn from the Household Survey of Entrepreneurship, 2003 (the survey, henceforth) recently made available by the Small Business Support (SBS) Unit at the Department of Trade and Industry (DTI). The real advantage of this data-set is that it allows for information on the individuals’ intentions to become self-employed in England and therefore it allows for assessing the import- ance of the perceived financial constraints on the future self-employment choice (or lack of). Empirically, I will use a variety of econometric methods to address the issues outlined above: I will first evaluate the extent to which financial con- straints have an adverse impact on the probability of being self-employed and then whether these are compounded by gender and ethnic background. Therefore, I will first estimate a probit model where the self-employment choice appears as the dependent variable; measures of financial constraints, gender and ethnicity (and their interactions with the finance constraints) appear among the regressors. One problem with this specification is the potential endogeneity of the financial constraints measures. Therefore I will try to address this problem by using self- selection models to solve this potential problem. So in the second model, I will try to quantify the extent to which our two main variables of interest (gender and ethnicity) have an impact only on the probability individuals have of experiencing financial constraints first, so that only those who do not experience financial con- straints can then become self-employed. This is equivalent to estimate a two-stage Heckman model where in the first stage I model the probability of experiencing financial constraints as a function of both gender and ethnic background, while in the second stage, I model the self-employment choice of those who have survived the first stage as a function of variables like the ability, human capital and so on.
Finally, I will test whether gender and ethnicity affect mainly the access to exter- nal finance, while the self-employment choice is influenced by other factors (like previous experience as self-employed, current employment status and so on) that are independent of both gender and ethnicity. Again this will involve the estima- tion of a two-stage Heckman model where in the first stage the probability of accessing external funding is affected by both gender and ethnicity, while in the second stage I model the probability of self-employment for the individuals that have approached external funders as a function of their previous experience, edu- cation and so on.
The rest of the chapter is organized as follows. Section 5.2 illustrates the structure of the empirical analysis. More specifically, Section 5.2.1 provides a theoretical framework that looks at how financial constraints, gender, ethnic background and self-employment choice are related to each other. The format of
the empirical analysis is also explained in detail in Section 5.2.2. Section 5.3 illustrates the data-set and provides a descriptive analysis of the main variables that will be used in the econometric analysis. Finally, Section 5.4 reports the main results, while Section 5.5 draws the main conclusions and gives an indica- tion of potential caveats to the empirical analysis.