Abstract
This study investigates the gender gap in entrepreneurial activity rates using the Conditional Mixed Process estimator (CMP) and controlling for the possible endogeneity of perceptual variables. We use the Global Entrepreneurship Monitor (GEM) adult population survey data for 12 countries. We find that the gender gap in activity rates shrinks drastically from −0.37 to −0.06 after controlling for observed traits, perceptual variables, and correcting for endogeneity using CMP. Our choice of instrument and estimation technique implies that CMP is more efficient and that unobserved factors still play a role in explaining the entrepreneurial decision. Unlike what is typically found in the literature that the gap disappears and becomes insignificant when endogeneity and control variables are added. However, in line with the argument that the gender gap in activity rates can be explained by skill perception and other covariates.
An earlier version of this draft was part of Sanaa’s Master’s thesis at Birzeit University; Economics Department.
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Notes
- 1.
The gender gap is often measured by the difference between female and male activity rates (for example total early stage entrepreneurial rate), or at times the ratio of male to female activity rates.
- 2.
This paper addresses single men and single women only, the implication is that the social status should be included in a regression relating to individual attributes to fear of failure. Daoud et al. (2015) found the social status dummy to be insignificant in fear of failure equation.
- 3.
- 4.
Except 2008 data of Palestine and Uganda, and 2009 data of Egypt.
- 5.
For more information about the variables and their expected sign see Table 1.
- 6.
The weak instrument test of Finlay and Magnusson (2009) could not be applied here, since it needs continuous dependent variable, while in this study the dependent variable (TEA) is binary.
- 7.
The correlation coefficient between Equalinc and the residuals is 0.02 compared to around 0.04 between Equalinc and skills, marginally higher.
- 8.
The two conditions: a variable that is not correlated with the residuals of the output but to be correlated with self-efficacy are found more applicable to Equalinc compared to the other variables that were correlated with the error term. However, using the other endogenous variables in the CMP model instead of Skills (as endogenous for TEA) yields nearly the same results without eliminating the gender gap.
- 9.
The parameter athanhrho represents an unbounded transformation of the usual rho-statistic. It is the arc-hyperbolic tangent of rho, and has the property of being unbounded compared to rho (rho is bounded in value between 1 and −1). Hence, it is suitable to be used as a base for testing the null hypothesis of no correlation between the error terms (Roodman 2009).
- 10.
We follow the same steps of Koellinger et al. (2013) to highlight any differences that may arise and to show later that unlike their finding, the gender gap in activity rates still remains negative and statistically significant after controlling for the individuals’ variables and correcting for the endogeneity problem. The CMP model yields more efficient results compared with bivariate probit model.
- 11.
Given that gender dummy is coded as (1: male, 2: female).
- 12.
The model selection criteria (AIC and BIC) as well as log likelihood and Pseudo R2 confirm improvement of model fit when moving across models 1–4.
- 13.
Self-efficacy records the highest correlation with the residuals (0.55) followed by know (0.44), opportunity (0.41), and fear of failure (−0.21).
- 14.
The gender gap slightly decreases, it is estimated to −0.057 in the CMP model compared to (−0.064) in the probit model and approximated to (−0.06).
- 15.
An entrepreneur is not just self-employed, the term is best described the cost of self-employment which is the wage and the cost of the entrepreneur who does not gain any profit. However, it is often used to mean business startup.
- 16.
Given that nearly 37% of the entrepreneurs in this study are not educated or have some secondary education.
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Acknowledgment
The authors would like to thank Thomas Schot, Shaker Sarsour, and Suhail Sultan for comments and feedback on earlier drafts of this paper.
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Kamal, S., Daoud, Y. (2018). Explaining the Gender Gap in Entrepreneurial Propensity. In: Faghih, N., Zali, M. (eds) Entrepreneurship Ecosystem in the Middle East and North Africa (MENA). Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-319-75913-5_11
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