5 Self-employment and gender
5.4 The results
Finally, it is possible to notice that the proportion of respondents located in the 15 per cent most deprived ward is very small and it appears that most Thinkers and Doers are located elsewhere. The only exception is for respondents of a Black background as they appear to be equally distributed between the most deprived wards and the other ones.
Table 5.8Model 1: Probit estimates VariablesModel 1Model 2Model 3Model 4 Coefficient T-ratioCoefficientT-ratioCoefficientT-ratioCoefficientT-ratio Dependent variable: Probability of being a Doer Previous experience0.2523.7600.2343323.5000.2323.430.2153.200 Attitude towards entrepreneurship−0.113−2.080−0.11239−2.030−0.112−1.95−0.112−1.940 Degree0.0530.960––0.0571– Employment status––−0.21472−1.020––−0.184−0.900 Age0.0712.4800.0685812.3600.0792.740.0792.700 Sex0.1061.9100.1061071.930––– White––––0.0140.13−0.000080.00000 Finance contraints*Sex−0.492−2.900−0.52819−3.310––– Finance constraints*White––––−0.248−2.41−0.242−2.300 Note N= 230. The observations are weighted by WEIGHT_1. We control for regions in all models.
location, it is necessary to qualify these results: indeed, the regional variable picks up all those local factors (like underdevelopment, presence of criminality, low level of economic activity and so on) that can have an adverse impact on the likelihood of running a successful business and that therefore are taken into consideration by lenders in deciding whether or not to fund an entrepreneurial project. In the first stage, the probability of being a Doer is not affected signific- antly by gender, but rather by the previous experience and by the attitude towards entrepreneurship. However, marginal effects (Table 5.10) are generally not significant, showing that from this sample it is not possible to draw conclusions regarding the whole population.
The results for the third model are presented in Table 5.11 while the marginal effects are presented in Table 5.12.
Notice that again the marginal effects are only presented for the second-stage equation. Generally speaking, the probability of having access to external finance increases if the respondent is male and white. This implies that women of white ethnic background decide to self-select themselves and prefer not to seek external finance. For the subset of individuals that decide to go for external Table 5.9 Model 2: Heckman two-stage model
Model 1
Coefficient T-ratio
Stage 2: Dep. Var. = DEP2ST
Previous experience 1.93 1.99
Degree 0.49 0.71
Attitude towards entrepreneurship −7.79 −5.29
Region −0.18 −1.72
Sex −0.25 −0.5
Stage 1: Dep. Var. = FC
Region 0.09 2.41
Degree 0.17 0.72
Landlord 0.02 4.17
Sex −1.13 −1.54
White −0.91 −1.61
Sex*White 1.29 1.69
Correlation coefficient −1.118 −1.52
Table 5.10 Model 2: Marginal effects
Coefficient T-ratio
Previous experience 0.011 1.32
Degree 0.003 0.66
Attitude towards entrepreneurship −0.073 −1.53
Region −0.001 −1.07
Sex −0.002 −0.39
finance, an improvement in the attitudes towards self-employment implies an increase of 2 per cent of the probability of becoming a Doer; also becoming unemployed means that the individual is less likely to become a Doer by 4 per cent. Finally, an increase of the previous experience is likely to increase the probability of becoming a Doer by 4 per cent. In these models, the regional and deprived area variables are not significant, showing that there is no location effect at work in either the self-selection mechanism or the self-employment Table 5.12 Model 3: Marginal effects
Model 1 Model 2 Model 3
Coefficient T-ratio Coefficient T-ratio Coefficient T-ratio
Previous experience 0.041 2.720 0.064 0.490 0.046 0.420
Degree 0.033 1.620 0.045 0.580 0.038 0.700
Attitude towards
entrepreneurship −0.029 −2.310 −0.040 −0.670 −0.023 −0.570 Employment status −0.047 −2.020 −0.049 −0.710 −0.033 −0.600
Region −0.001 −0.240 −0.004 −0.290 −0.002 −0.150
Table 5.11 Model 3: Heckman two-stage model
Model 1 Model 2 Model 3
Coefficient T-ratio Coefficient T-ratio Coefficient T-ratio Dependent variable: DEP2ST
Previous experience 0.261 2.940 0.366 0.680 0.287 0.520
Degree 0.199 1.730 0.245 0.880 0.226 1.010
Attitude towards
entrepreneurship −0.174 −2.550 −0.213 −1.070 −0.138 −0.780 Employment status −0.295 −2.040 −0.280 −1.040 −0.207 −0.780
Region −0.006 −0.240 −0.021 −0.350 −0.014 −0.160
Constant −1.632 −9.410 −1.609 −7.570 −1.649 −8.580
Dependent variable: DEP1ST
Region 0.002 0.120 0.005 0.290 0.003 0.170
Degree 0.096 0.990 0.094 0.980 0.101 1.030
Sex 0.387 2.540 −0.118 −1.030 −0.073 −0.490
White 0.233 3.270 – – – –
Asian – – – – −0.397 −0.910
Black – – −0.054 −0.260 – –
Sex*White −0.529 −3.170 – – – –
Sex*Black – – 0.575 1.260 – –
Sex*Asian – – – – 0.440 1.150
Constant −1.460 −10.290 −1.264 −9.700 −1.244 −9.650
Correlation coefficient 3.803 2.200 2.266 0.850 2.844 0.490
Note
Initial number of observations: N = 2106. The observations are weighted by WEIGHT_1.
choice. I have tried to run these same specifications on respondents of different ethnic background but I have not been able to find significant results. This sug- gests that in the case of Black and Asian minorities other factors are at work that cannot be captured adequately by this type of model.
Altogether these results confirm my initial hypothesis. In my population, women (from any ethnic background) do not appear to be financially con- strained because of their gender but only because of the lack of collateral; also, in the population, the expectation of being financially constrained in the future deters women from seeking external finance for their investment projects. These results confirm my initial hypothesis, namely that gender and ethnic background condition the probability of seeking for external finance rather than the probab- ility of being financially constrained. Of course, these results do not identify exactly why this is the case. Consistently with my view on how financial con- straints affect economic outcomes, I can only conjecture that this is so because female applicants anticipate that most of the surplus generated by the entrepre- neurial project will be appropriated back by the lender. Indeed, it would be inter- esting to devise an empirical strategy that allows to test whether this type of mechanism is at work in the population.
Some caveats to the empirical analysis are important and they mostly arise from the data I have used. Also, the employment status variable for the Doers does seem to have a problem as most Doers are classified as unemployed.
Second, Black and Asian communities seem to be under-represented in the sample. This point may simply reflect the proportion in the population of indi- viduals from both the Black and the Asian communities. To clarify this point, it would be helpful to have some information on the whole population and on the population of entrepreneurs. Third, this analysis of the survey is, because of the way the sample is constructed, more focused on “Thinkers” and
“Doers”, i.e. those who are either engaged in or thinking about undertaking some enterprise activity. I have not considered at all the individuals who claim not to be interested in becoming self-employed (the so-called Avoiders); this is mostly due to data availability as they are not asked the same type of ques- tions on external finance and experienced financial constraints as the Doers and Thinkers do. However, it would be advisable to devise questions for the Avoiders that mirror those for the two other segments of the sample. Also, the data analysed here are purely cross-sectional, however, and while this makes it possible to draw some inferences about the effects of financial constraints on start-up rates, for example, it is impossible to draw implications on the unob- served heterogeneity among individuals that clearly affects whether applicants experience financial constraints and then their self-employment choice. In other words, financial constraints may also depend on the unobserved hetero- geneity of the individuals in the sample and the use of cross-sectional data pre- vents from controlling for all these additional factors that may affect financial constraints. Also, cross-sectional data do not make it possible to control the impact of financial constraints on the subsequent success of the start-up com- panies. Similarly, it proved difficult from the existing survey data to draw any
firm conclusion about the impact of finance shortages on subsequent business performance. Both require more longitudinal follow-up of individuals that have participated in cross-sectional surveys and this as a research priority. In each case, sample sizes were relatively small and inferences about either group were unlikely to be robust. The policy significance of each issue is likely to mean that developing appropriate sampling methodologies to identify ethnicity and spatial effects on financial barriers are also likely to be issues for the future. Finally, it is worth noting that in each of these general surveys the pro- portion of firms and individuals reporting that they were involved in entrepre- neurship and that they had experienced difficulties in accessing finance is relatively small.