Once an entrepreneur, always an entrepreneur? The impacts of skills developed before, during and after college on firm start-ups

Orazem, Peter F; Jolly, Robert; Yu, Li

doi:10.1186/s40172-015-0023-7

Table 9 Ordered logit model of returns to early and late entrepreneurship

From: Once an entrepreneur, always an entrepreneur? The impacts of skills developed before, during and after college on firm start-ups

	Model (1)	Model (2)	Model (3)	Model (4)	Model (5)
	Unconditional annual income	Expanded Model (1)	Model (2) + occupations and industries	Persisting laborer	Persisting entrepreneurs
(Laborer, entrepreneur)	−0.226	−0.358	−0.411
	(1.39)	(1.96)**	(2.26)**
(Entrepreneurs, laborer)	−0.467	−0.834	−0.924
	(1.90)*	(3.33)***	(3.63)***
(Entrepreneur, entrepreneur)	−0.253	0.185	0.121
	(1.20)	(0.68)	(0.43)
AC_SPEC		−0.009	−0.003	−0.001	−0.116
		(2.12)**	(0.68)	(0.19)	(2.58)***
OCCUPATIONS		−0.012	−0.067	−0.025	−0.728
		(0.39)	(2.07)**	(0.70)	(2.83)***
INDUSTRIES		−0.074	−0.072	−0.067	0.358
		(4.24)***	(4.04)***	(3.60)***	(1.69)*
A professional degree		0.462	0.477	0.465	2.602
		(6.30)***	(6.28)***	(5.76)***	(3.19)***
Male		1.294	1.157	1.095	4.098
		(17.32)***	(15.17)***	(13.48)***	(4.33)***
Married		0.316	0.314	0.296	0.542
		(4.69)***	(4.53)***	(4.01)***	(0.57)
Ethnicity		−0.126	−0.086	−0.140	2.116
		(1.09)	(0.69)	(1.07)	(1.86)*
Age		0.259	0.262	0.284	−0.056
		(4.68)***	(5.00)***	(4.99)***	(0.05)
Age²		−0.003	−0.003	−0.003	0.001
		(4.74)***	(4.88)***	(4.84)***	(0.11)
High school class rank		−0.001	−0.001	0.000	−0.017
		(0.58)	(0.75)	(0.02)	(0.87)
Cumulative GPA		0.097	0.139	0.156	0.915
		(1.54)	(2.11)**	(2.22)**	(1.00)
Number of siblings		0.007	0.007	0.012	−0.054
		(0.32)	(0.33)	(0.49)	(0.19)
Grow up with two parents in household		0.065	0.044	0.081	0.168
		(0.64)	(0.42)	(0.74)	(0.11)
Father education		0.018	0.018	0.031	0.559
		(0.77)	(0.74)	(1.22)	(1.80)*
Mother education		−0.016	−0.017	−0.016	−0.355
		(0.62)	(0.64)	(0.58)	(1.01)
Either of parents started a business		0.222	0.196	0.221	0.600
		(3.42)***	(2.95)***	(3.12)***	(0.65)
Close friend started a business		−0.174	−0.198	−0.203	−0.421
		(2.62)***	(2.94)***	(2.84)***	(0.44)
Agriculture and Life Sciences		0.116	0.026	−0.064	3.482
		(1.01)	(0.22)	(0.48)	(2.10)**
Business		0.761	0.506	0.547	3.798
		(6.61)***	(4.25)***	(4.30)***	(1.49)
Design		0.272	0.301	0.178	5.940
		(2.11)**	(2.17)**	(1.18)	(3.95)***
Engineering		1.128	1.046	1.071	4.434
		(11.62)***	(10.14)***	(9.82)***	(2.76)***
Human Sciences		−0.194	−0.126	−0.140	5.288
		(1.66)*	(1.03)	(1.11)	(3.53)***
1987-1991		−0.239	−0.151	−0.140	−4.001
		(1.73)*	(1.05)	(0.90)	(2.09)**
1992-1996		−0.452	−0.324	−0.318	−5.856
		(3.00)***	(2.08)**	(1.89)*	(3.19)***
1997-2001		−0.814	−0.678	−0.672	−5.056
		(4.36)***	(3.46)***	(3.20)***	(2.26)**
2002-2006		−1.318	−1.144	−1.191	−4.975
		(5.13)***	(4.31)***	(4.23)***	(1.53)
Doing things differently		−0.027	0.010	−0.048	2.270
		(0.31)	(0.11)	(0.49)	(2.62)***
EXDIV		0.012	0.007	0.006	−0.235
		(0.48)	(0.28)	(0.23)	(0.54)
Foreign languages		0.191	0.190	0.138	2.105
		(1.74)*	(1.75)*	(1.17)	(1.67)*
Other foreign languages		0.182	0.202	0.202	0.603
	−0.226	−0.358	−0.411
Observations	4981	4531	4531	3933	130

Note: Dependent variable is annual income, which is categorical with one indicating less than $25,000 and eight indicating more than $500,000. To save space, the estimated cutoff points are not reported. The cutoff points are jointly significant at 1% level. Models (2) – (5) also control for the individual’s current occupation and industry. We also use the USDA Rural–urban Continuum Codes (RUCC, http://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx) at the county level to control for the degree of urbanization and adjacency to a metro area. Binary industry variables and occupations are shown in Additional file 1: Table S1.

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