Does more education lead to better startup funding outcomes?

Figures & data

Table 1. Summary statistics and correlations.

Variable	M	SD	1	2	3	4	5	6	7	8	9
1. funded	0.208	0.489	1.000
2. ed yrs	4.806	2.218	−0.017	1.000
3. ed yrs sq	27.944	19.593	−0.095	0.963	1.000
4. founder age	48.264	12.879	−0.216	−0.055	−0.013	1.000
5. c-corp	0.444	0.500	0.229	0.104	0.074	−0.093	1.000
6. revenue	0.514	0.503	0.020	0.040	−0.050	−0.076	−0.081	1.000
7. patents filed	1.736	5.528	0.131	0.196	0.237	0.042	−0.089	0.085	1.000
8. bod mbrs	0.986	1.614	0.431	−0.064	−0.049	0.044	0.426	0.061	0.273	1.000
9. patent × bod	4.111	16.531	0.115	0.146	0.190	0.023	−0.062	0.137	0.953	0.382	1.000

Figure 1. Distribution of funding successes across education years beyond high school.

Table 2. Determinants of professional funding for startups.

Variable	(1)	(2)	(3)	(4)
ed yrs	1.0028**	1.0366**		2.4158**
ed yrs sq	−0.1217**	−0.1260**		−0.2811**
founder age		−0.0267*		−0.0624**
c-corp			0.1733	−0.0995
revenue			0.0846	−0.1877
patents filed			0.2007*	0.2537*
bod mbrs			0.4325***	0.7466***
patent x bod			−0.0693*	−0.0709
constant	−2.3947***	−1.2129	−1.5873***	−3.4078
Pseudo-R^2	0.1104	0.1519	0.2184	0.5075

Note. Each of the four models are probit models with professional investor funding as the Boolean dependent variable. ∗, ∗∗, and ∗∗∗ indicate that the coefficients are statistically significantly different from zero (based on p-values) at the 10, 5, and 1% level, respectively.

Data availability

The data that support the findings of this study are available from the corresponding author, Craig R. Everett, upon reasonable request. This data set contains confidential financial data from startup companies. Every effort has been made to redact any identifying information from each record, but there still may be ways, in some cases, to deduce the identity of the companies or founders. Thus, any requests for data must be solely for academic purposes.